{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "data = pd.read_csv(\"C:/Users/15905/documents/citydata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data.loc[0:17, ['公路货运量','进出口贸易总额','是否为省会','是否有沿海港口或内河港口','民用航空']]\n",
    "train_data.shape\n",
    "test_data=data.loc[18:40,['公路货运量','进出口贸易总额','是否为省会','是否有沿海港口或内河港口','民用航空']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_targets = data.loc[0:17, ['专家评分2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data=pd.DataFrame(train_data,dtype=float)\n",
    "train_targets=pd.DataFrame(train_targets,dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = train_data.mean(axis=0)\n",
    "train_data -= mean\n",
    "std = train_data.std(axis=0)\n",
    "train_data /= std\n",
    "test_data -= mean\n",
    "test_data /= std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow`",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mc:\\users\\15905\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mRandomRotation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'tensorflow.keras.layers.experimental.preprocessing'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-bd736bfdfede>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menviron\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"CUDA_VISIBLE_DEVICES\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"1\"\u001b[0m   \u001b[1;31m# GTX 1050 Ti\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlayers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\15905\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     raise ImportError(\n\u001b[1;32m----> 6\u001b[1;33m         \u001b[1;34m'Keras requires TensorFlow 2.2 or higher. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m         'Install TensorFlow via `pip install tensorflow`')\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mImportError\u001b[0m: Keras requires TensorFlow 2.2 or higher. Install TensorFlow via `pip install tensorflow`"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"   # GTX 1050 Ti\n",
    "\n",
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "\n",
    "def build_model():\n",
    "    model = models.Sequential()\n",
    "    model.add(layers.Dense(5, activation='relu',\n",
    "                           input_shape=(train_data.shape[1],)))\n",
    "    model.add(layers.Dense(3, activation='relu'))\n",
    "    model.add(layers.Dense(1))\n",
    "    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "train_data=train_data.fillna(value=0)\n",
    "k = 4\n",
    "num_val_samples = len(train_data) // k\n",
    "num_epochs = 100\n",
    "all_scores = []\n",
    "for i in range(k):\n",
    "    print('processing fold #', i)\n",
    "    # 准备验证数据：第 k 个分区的数据\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "\n",
    "    # 准备训练数据：其他所有分区的数据\n",
    "    partial_train_data = np.concatenate(\n",
    "        [train_data[:i * num_val_samples],\n",
    "         train_data[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "        [train_targets[:i * num_val_samples],\n",
    "         train_targets[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "\n",
    "    # 构建 Keras 模型（已编译）\n",
    "    model = build_model()\n",
    "    model.fit(partial_train_data, partial_train_targets,\n",
    "              epochs=num_epochs, batch_size=1, verbose=1)\n",
    "    # 在验证数据上评估模型\n",
    "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
    "    all_scores.append(val_mae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.mean(all_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing fold # 0\n",
      "Epoch 1/500\n",
      " 1/14 [=>............................] - ETA: 0s - loss: 13.0320 - mae: 3.6100WARNING:tensorflow:Callbacks method `on_test_batch_end` is slow compared to the batch time (batch time: 0.0000s vs `on_test_batch_end` time: 0.0010s). Check your callbacks.\n",
      "14/14 [==============================] - 0s 15ms/step - loss: 7.8377 - mae: 2.2850 - val_loss: 1.7360 - val_mae: 1.2427\n",
      "Epoch 2/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 7.5931 - mae: 2.2421 - val_loss: 1.6775 - val_mae: 1.2171\n",
      "Epoch 3/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 7.4011 - mae: 2.1980 - val_loss: 1.6300 - val_mae: 1.1958\n",
      "Epoch 4/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 7.2164 - mae: 2.1639 - val_loss: 1.5829 - val_mae: 1.1739\n",
      "Epoch 5/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 7.0453 - mae: 2.1272 - val_loss: 1.5399 - val_mae: 1.1528\n",
      "Epoch 6/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.8825 - mae: 2.0877 - val_loss: 1.5044 - val_mae: 1.1350\n",
      "Epoch 7/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.7303 - mae: 2.0563 - val_loss: 1.4610 - val_mae: 1.1143\n",
      "Epoch 8/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.5802 - mae: 2.0260 - val_loss: 1.4186 - val_mae: 1.0940\n",
      "Epoch 9/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.4400 - mae: 1.9899 - val_loss: 1.3798 - val_mae: 1.0758\n",
      "Epoch 10/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.3031 - mae: 1.9566 - val_loss: 1.3431 - val_mae: 1.0585\n",
      "Epoch 11/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.1698 - mae: 1.9229 - val_loss: 1.3069 - val_mae: 1.0410\n",
      "Epoch 12/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.0324 - mae: 1.8905 - val_loss: 1.2674 - val_mae: 1.0218\n",
      "Epoch 13/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.8831 - mae: 1.8576 - val_loss: 1.2241 - val_mae: 0.9991\n",
      "Epoch 14/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.7421 - mae: 1.8209 - val_loss: 1.1862 - val_mae: 0.9784\n",
      "Epoch 15/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.6028 - mae: 1.7873 - val_loss: 1.1417 - val_mae: 0.9533\n",
      "Epoch 16/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.4778 - mae: 1.7448 - val_loss: 1.1031 - val_mae: 0.9311\n",
      "Epoch 17/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.3524 - mae: 1.7158 - val_loss: 1.0613 - val_mae: 0.9064\n",
      "Epoch 18/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.2209 - mae: 1.6853 - val_loss: 1.0183 - val_mae: 0.8802\n",
      "Epoch 19/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.1014 - mae: 1.6567 - val_loss: 0.9845 - val_mae: 0.8587\n",
      "Epoch 20/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.9842 - mae: 1.6316 - val_loss: 0.9449 - val_mae: 0.8326\n",
      "Epoch 21/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.8651 - mae: 1.6020 - val_loss: 0.9101 - val_mae: 0.8089\n",
      "Epoch 22/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.7555 - mae: 1.5764 - val_loss: 0.8768 - val_mae: 0.7856\n",
      "Epoch 23/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.6540 - mae: 1.5456 - val_loss: 0.8448 - val_mae: 0.7622\n",
      "Epoch 24/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.5433 - mae: 1.5252 - val_loss: 0.8113 - val_mae: 0.7372\n",
      "Epoch 25/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.4435 - mae: 1.4940 - val_loss: 0.7820 - val_mae: 0.7142\n",
      "Epoch 26/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.3479 - mae: 1.4642 - val_loss: 0.7534 - val_mae: 0.6906\n",
      "Epoch 27/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.2507 - mae: 1.4411 - val_loss: 0.7282 - val_mae: 0.6687\n",
      "Epoch 28/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1627 - mae: 1.4134 - val_loss: 0.7011 - val_mae: 0.6447\n",
      "Epoch 29/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.0772 - mae: 1.3838 - val_loss: 0.6791 - val_mae: 0.6239\n",
      "Epoch 30/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9940 - mae: 1.3593 - val_loss: 0.6553 - val_mae: 0.6167\n",
      "Epoch 31/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9148 - mae: 1.3344 - val_loss: 0.6327 - val_mae: 0.6098\n",
      "Epoch 32/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8423 - mae: 1.3120 - val_loss: 0.6129 - val_mae: 0.6030\n",
      "Epoch 33/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 3.7748 - mae: 1.2909 - val_loss: 0.5948 - val_mae: 0.5969\n",
      "Epoch 34/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 3.7119 - mae: 1.2730 - val_loss: 0.5764 - val_mae: 0.5900\n",
      "Epoch 35/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 3.6510 - mae: 1.2557 - val_loss: 0.5586 - val_mae: 0.5830\n",
      "Epoch 36/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 3.5882 - mae: 1.2411 - val_loss: 0.5421 - val_mae: 0.5764\n",
      "Epoch 37/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 3.5345 - mae: 1.2240 - val_loss: 0.5276 - val_mae: 0.5701\n",
      "Epoch 38/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4799 - mae: 1.2141 - val_loss: 0.5122 - val_mae: 0.5625\n",
      "Epoch 39/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4307 - mae: 1.2145 - val_loss: 0.4993 - val_mae: 0.5564\n",
      "Epoch 40/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3860 - mae: 1.2091 - val_loss: 0.4856 - val_mae: 0.5488\n",
      "Epoch 41/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3413 - mae: 1.2030 - val_loss: 0.4740 - val_mae: 0.5422\n",
      "Epoch 42/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2982 - mae: 1.2067 - val_loss: 0.4615 - val_mae: 0.5342\n",
      "Epoch 43/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2596 - mae: 1.1983 - val_loss: 0.4511 - val_mae: 0.5271\n",
      "Epoch 44/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2201 - mae: 1.1991 - val_loss: 0.4414 - val_mae: 0.5200\n",
      "Epoch 45/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1846 - mae: 1.1987 - val_loss: 0.4327 - val_mae: 0.5131\n",
      "Epoch 46/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1538 - mae: 1.1910 - val_loss: 0.4238 - val_mae: 0.5058\n",
      "Epoch 47/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1165 - mae: 1.1967 - val_loss: 0.4159 - val_mae: 0.4985\n",
      "Epoch 48/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0908 - mae: 1.1876 - val_loss: 0.4075 - val_mae: 0.4901\n",
      "Epoch 49/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0591 - mae: 1.1893 - val_loss: 0.4010 - val_mae: 0.4830\n",
      "Epoch 50/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0285 - mae: 1.1902 - val_loss: 0.3947 - val_mae: 0.4757\n",
      "Epoch 51/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0028 - mae: 1.1847 - val_loss: 0.3883 - val_mae: 0.4678\n",
      "Epoch 52/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9757 - mae: 1.1798 - val_loss: 0.3830 - val_mae: 0.4599\n",
      "Epoch 53/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9463 - mae: 1.1816 - val_loss: 0.3782 - val_mae: 0.4519\n",
      "Epoch 54/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9166 - mae: 1.1669 - val_loss: 0.3739 - val_mae: 0.4439\n",
      "Epoch 55/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8910 - mae: 1.1671 - val_loss: 0.3700 - val_mae: 0.4362\n",
      "Epoch 56/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8667 - mae: 1.1676 - val_loss: 0.3648 - val_mae: 0.4261\n",
      "Epoch 57/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8418 - mae: 1.1621 - val_loss: 0.3617 - val_mae: 0.4180\n",
      "Epoch 58/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8175 - mae: 1.1582 - val_loss: 0.3595 - val_mae: 0.4210\n",
      "Epoch 59/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7921 - mae: 1.1533 - val_loss: 0.3567 - val_mae: 0.4295\n",
      "Epoch 60/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7653 - mae: 1.1590 - val_loss: 0.3544 - val_mae: 0.4373\n",
      "Epoch 61/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7439 - mae: 1.1621 - val_loss: 0.3537 - val_mae: 0.4450\n",
      "Epoch 62/500\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 2.7249 - mae: 1.1572 - val_loss: 0.3521 - val_mae: 0.4526\n",
      "Epoch 63/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.6998 - mae: 1.1625 - val_loss: 0.3515 - val_mae: 0.4605\n",
      "Epoch 64/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6790 - mae: 1.1594 - val_loss: 0.3500 - val_mae: 0.4681\n",
      "Epoch 65/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6501 - mae: 1.1662 - val_loss: 0.3490 - val_mae: 0.4744\n",
      "Epoch 66/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6331 - mae: 1.1671 - val_loss: 0.3494 - val_mae: 0.4814\n",
      "Epoch 67/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6130 - mae: 1.1629 - val_loss: 0.3502 - val_mae: 0.4902\n",
      "Epoch 68/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5878 - mae: 1.1660 - val_loss: 0.3501 - val_mae: 0.4970\n",
      "Epoch 69/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5667 - mae: 1.1641 - val_loss: 0.3514 - val_mae: 0.5039\n",
      "Epoch 70/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5468 - mae: 1.1585 - val_loss: 0.3529 - val_mae: 0.5117\n",
      "Epoch 71/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5235 - mae: 1.1623 - val_loss: 0.3548 - val_mae: 0.5189\n",
      "Epoch 72/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5009 - mae: 1.1580 - val_loss: 0.3574 - val_mae: 0.5270\n",
      "Epoch 73/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4770 - mae: 1.1650 - val_loss: 0.3588 - val_mae: 0.5328\n",
      "Epoch 74/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4577 - mae: 1.1583 - val_loss: 0.3633 - val_mae: 0.5411\n",
      "Epoch 75/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4377 - mae: 1.1514 - val_loss: 0.3673 - val_mae: 0.5486\n",
      "Epoch 76/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4148 - mae: 1.1583 - val_loss: 0.3727 - val_mae: 0.5573\n",
      "Epoch 77/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3898 - mae: 1.1507 - val_loss: 0.3789 - val_mae: 0.5657\n",
      "Epoch 78/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3692 - mae: 1.1506 - val_loss: 0.3823 - val_mae: 0.5724\n",
      "Epoch 79/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3496 - mae: 1.1477 - val_loss: 0.3884 - val_mae: 0.5800\n",
      "Epoch 80/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3262 - mae: 1.1448 - val_loss: 0.3957 - val_mae: 0.5879\n",
      "Epoch 81/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3065 - mae: 1.1507 - val_loss: 0.4022 - val_mae: 0.5946\n",
      "Epoch 82/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2871 - mae: 1.1504 - val_loss: 0.4091 - val_mae: 0.6025\n",
      "Epoch 83/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2619 - mae: 1.1518 - val_loss: 0.4118 - val_mae: 0.6068\n",
      "Epoch 84/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2441 - mae: 1.1474 - val_loss: 0.4202 - val_mae: 0.6144\n",
      "Epoch 85/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2247 - mae: 1.1433 - val_loss: 0.4301 - val_mae: 0.6229\n",
      "Epoch 86/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1990 - mae: 1.1423 - val_loss: 0.4389 - val_mae: 0.6299\n",
      "Epoch 87/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1835 - mae: 1.1396 - val_loss: 0.4487 - val_mae: 0.6376\n",
      "Epoch 88/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1572 - mae: 1.1368 - val_loss: 0.4551 - val_mae: 0.6432\n",
      "Epoch 89/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1435 - mae: 1.1301 - val_loss: 0.4655 - val_mae: 0.6505\n",
      "Epoch 90/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1202 - mae: 1.1286 - val_loss: 0.4766 - val_mae: 0.6582\n",
      "Epoch 91/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1007 - mae: 1.1174 - val_loss: 0.4887 - val_mae: 0.6661\n",
      "Epoch 92/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0760 - mae: 1.1247 - val_loss: 0.4977 - val_mae: 0.6720\n",
      "Epoch 93/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0615 - mae: 1.1206 - val_loss: 0.5123 - val_mae: 0.6806\n",
      "Epoch 94/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0371 - mae: 1.1223 - val_loss: 0.5244 - val_mae: 0.6876\n",
      "Epoch 95/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0241 - mae: 1.1118 - val_loss: 0.5371 - val_mae: 0.6948\n",
      "Epoch 96/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0005 - mae: 1.1126 - val_loss: 0.5514 - val_mae: 0.7024\n",
      "Epoch 97/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9848 - mae: 1.1057 - val_loss: 0.5655 - val_mae: 0.7098\n",
      "Epoch 98/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9607 - mae: 1.1028 - val_loss: 0.5790 - val_mae: 0.7166\n",
      "Epoch 99/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9389 - mae: 1.1052 - val_loss: 0.5912 - val_mae: 0.7226\n",
      "Epoch 100/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9315 - mae: 1.0992 - val_loss: 0.6040 - val_mae: 0.7286\n",
      "Epoch 101/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9070 - mae: 1.0954 - val_loss: 0.6233 - val_mae: 0.7374\n",
      "Epoch 102/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8883 - mae: 1.0947 - val_loss: 0.6398 - val_mae: 0.7448\n",
      "Epoch 103/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8705 - mae: 1.0909 - val_loss: 0.6586 - val_mae: 0.7528\n",
      "Epoch 104/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8539 - mae: 1.0891 - val_loss: 0.6783 - val_mae: 0.7611\n",
      "Epoch 105/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8343 - mae: 1.0834 - val_loss: 0.6990 - val_mae: 0.7694\n",
      "Epoch 106/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8174 - mae: 1.0844 - val_loss: 0.7139 - val_mae: 0.7752\n",
      "Epoch 107/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7974 - mae: 1.0839 - val_loss: 0.7346 - val_mae: 0.7832\n",
      "Epoch 108/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7815 - mae: 1.0768 - val_loss: 0.7555 - val_mae: 0.7910\n",
      "Epoch 109/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7651 - mae: 1.0746 - val_loss: 0.7719 - val_mae: 0.7968\n",
      "Epoch 110/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7483 - mae: 1.0694 - val_loss: 0.7904 - val_mae: 0.8033\n",
      "Epoch 111/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7322 - mae: 1.0644 - val_loss: 0.8120 - val_mae: 0.8110\n",
      "Epoch 112/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7109 - mae: 1.0675 - val_loss: 0.8298 - val_mae: 0.8169\n",
      "Epoch 113/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6962 - mae: 1.0668 - val_loss: 0.8528 - val_mae: 0.8246\n",
      "Epoch 114/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6784 - mae: 1.0606 - val_loss: 0.8696 - val_mae: 0.8298\n",
      "Epoch 115/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6659 - mae: 1.0538 - val_loss: 0.8930 - val_mae: 0.8374\n",
      "Epoch 116/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6513 - mae: 1.0474 - val_loss: 0.9158 - val_mae: 0.8446\n",
      "Epoch 117/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6293 - mae: 1.0450 - val_loss: 0.9336 - val_mae: 0.8498\n",
      "Epoch 118/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6194 - mae: 1.0460 - val_loss: 0.9567 - val_mae: 0.8570\n",
      "Epoch 119/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6072 - mae: 1.0367 - val_loss: 0.9823 - val_mae: 0.8645\n",
      "Epoch 120/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5856 - mae: 1.0317 - val_loss: 1.0009 - val_mae: 0.8694\n",
      "Epoch 121/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5763 - mae: 1.0282 - val_loss: 1.0271 - val_mae: 0.8770\n",
      "Epoch 122/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5598 - mae: 1.0254 - val_loss: 1.0566 - val_mae: 0.8897\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 123/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5417 - mae: 1.0268 - val_loss: 1.0804 - val_mae: 0.9029\n",
      "Epoch 124/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5323 - mae: 1.0203 - val_loss: 1.1077 - val_mae: 0.9178\n",
      "Epoch 125/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5169 - mae: 1.0132 - val_loss: 1.1259 - val_mae: 0.9287\n",
      "Epoch 126/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5055 - mae: 1.0166 - val_loss: 1.1502 - val_mae: 0.9421\n",
      "Epoch 127/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4923 - mae: 1.0089 - val_loss: 1.1729 - val_mae: 0.9539\n",
      "Epoch 128/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4802 - mae: 1.0007 - val_loss: 1.2038 - val_mae: 0.9699\n",
      "Epoch 129/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4631 - mae: 1.0010 - val_loss: 1.2187 - val_mae: 0.9784\n",
      "Epoch 130/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4526 - mae: 0.9939 - val_loss: 1.2441 - val_mae: 0.9913\n",
      "Epoch 131/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4400 - mae: 0.9907 - val_loss: 1.2599 - val_mae: 1.0002\n",
      "Epoch 132/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4356 - mae: 0.9812 - val_loss: 1.2847 - val_mae: 1.0124\n",
      "Epoch 133/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4185 - mae: 0.9793 - val_loss: 1.3132 - val_mae: 1.0267\n",
      "Epoch 134/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4048 - mae: 0.9739 - val_loss: 1.3412 - val_mae: 1.0403\n",
      "Epoch 135/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3912 - mae: 0.9747 - val_loss: 1.3496 - val_mae: 1.0451\n",
      "Epoch 136/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3793 - mae: 0.9663 - val_loss: 1.3764 - val_mae: 1.0575\n",
      "Epoch 137/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3684 - mae: 0.9605 - val_loss: 1.4049 - val_mae: 1.0709\n",
      "Epoch 138/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3563 - mae: 0.9560 - val_loss: 1.4311 - val_mae: 1.0830\n",
      "Epoch 139/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3418 - mae: 0.9501 - val_loss: 1.4584 - val_mae: 1.0955\n",
      "Epoch 140/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3331 - mae: 0.9442 - val_loss: 1.4749 - val_mae: 1.1036\n",
      "Epoch 141/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3249 - mae: 0.9388 - val_loss: 1.4969 - val_mae: 1.1134\n",
      "Epoch 142/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3082 - mae: 0.9413 - val_loss: 1.5083 - val_mae: 1.1192\n",
      "Epoch 143/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3018 - mae: 0.9305 - val_loss: 1.5333 - val_mae: 1.1302\n",
      "Epoch 144/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2873 - mae: 0.9249 - val_loss: 1.5552 - val_mae: 1.1406\n",
      "Epoch 145/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2838 - mae: 0.9249 - val_loss: 1.5889 - val_mae: 1.1551\n",
      "Epoch 146/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2720 - mae: 0.9238 - val_loss: 1.6073 - val_mae: 1.1631\n",
      "Epoch 147/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2638 - mae: 0.9137 - val_loss: 1.6341 - val_mae: 1.1748\n",
      "Epoch 148/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2505 - mae: 0.9080 - val_loss: 1.6575 - val_mae: 1.1844\n",
      "Epoch 149/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2420 - mae: 0.9061 - val_loss: 1.6674 - val_mae: 1.1889\n",
      "Epoch 150/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2397 - mae: 0.8989 - val_loss: 1.6857 - val_mae: 1.1966\n",
      "Epoch 151/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2279 - mae: 0.8962 - val_loss: 1.7103 - val_mae: 1.2066\n",
      "Epoch 152/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2171 - mae: 0.8921 - val_loss: 1.7186 - val_mae: 1.2104\n",
      "Epoch 153/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2151 - mae: 0.8905 - val_loss: 1.7261 - val_mae: 1.2133\n",
      "Epoch 154/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2037 - mae: 0.8840 - val_loss: 1.7410 - val_mae: 1.2199\n",
      "Epoch 155/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.2019 - mae: 0.8769 - val_loss: 1.7620 - val_mae: 1.2279\n",
      "Epoch 156/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1905 - mae: 0.8776 - val_loss: 1.7790 - val_mae: 1.2349\n",
      "Epoch 157/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1827 - mae: 0.8700 - val_loss: 1.8000 - val_mae: 1.2434\n",
      "Epoch 158/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1777 - mae: 0.8727 - val_loss: 1.8114 - val_mae: 1.2480\n",
      "Epoch 159/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1717 - mae: 0.8603 - val_loss: 1.8309 - val_mae: 1.2558\n",
      "Epoch 160/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1619 - mae: 0.8554 - val_loss: 1.8525 - val_mae: 1.2643\n",
      "Epoch 161/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1586 - mae: 0.8514 - val_loss: 1.8716 - val_mae: 1.2719\n",
      "Epoch 162/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1528 - mae: 0.8452 - val_loss: 1.8870 - val_mae: 1.2779\n",
      "Epoch 163/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1433 - mae: 0.8429 - val_loss: 1.8984 - val_mae: 1.2822\n",
      "Epoch 164/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1383 - mae: 0.8425 - val_loss: 1.9092 - val_mae: 1.2864\n",
      "Epoch 165/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1340 - mae: 0.8355 - val_loss: 1.9199 - val_mae: 1.2905\n",
      "Epoch 166/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1311 - mae: 0.8280 - val_loss: 1.9281 - val_mae: 1.2936\n",
      "Epoch 167/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1208 - mae: 0.8260 - val_loss: 1.9360 - val_mae: 1.2965\n",
      "Epoch 168/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1201 - mae: 0.8229 - val_loss: 1.9543 - val_mae: 1.3035\n",
      "Epoch 169/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1123 - mae: 0.8219 - val_loss: 1.9637 - val_mae: 1.3070\n",
      "Epoch 170/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1069 - mae: 0.8148 - val_loss: 1.9790 - val_mae: 1.3126\n",
      "Epoch 171/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.1002 - mae: 0.8160 - val_loss: 1.9871 - val_mae: 1.3155\n",
      "Epoch 172/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0958 - mae: 0.8068 - val_loss: 2.0000 - val_mae: 1.3200\n",
      "Epoch 173/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0899 - mae: 0.8040 - val_loss: 2.0088 - val_mae: 1.3233\n",
      "Epoch 174/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0877 - mae: 0.7948 - val_loss: 2.0177 - val_mae: 1.3264\n",
      "Epoch 175/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0817 - mae: 0.7937 - val_loss: 2.0300 - val_mae: 1.3308\n",
      "Epoch 176/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0737 - mae: 0.7924 - val_loss: 2.0465 - val_mae: 1.3369\n",
      "Epoch 177/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0725 - mae: 0.7876 - val_loss: 2.0531 - val_mae: 1.3391\n",
      "Epoch 178/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0679 - mae: 0.7817 - val_loss: 2.0799 - val_mae: 1.3490\n",
      "Epoch 179/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0617 - mae: 0.7782 - val_loss: 2.0876 - val_mae: 1.3516\n",
      "Epoch 180/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0561 - mae: 0.7765 - val_loss: 2.1059 - val_mae: 1.3581\n",
      "Epoch 181/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0568 - mae: 0.7711 - val_loss: 2.1084 - val_mae: 1.3590\n",
      "Epoch 182/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0468 - mae: 0.7670 - val_loss: 2.1155 - val_mae: 1.3614\n",
      "Epoch 183/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0422 - mae: 0.7646 - val_loss: 2.1194 - val_mae: 1.3628\n",
      "Epoch 184/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0444 - mae: 0.7633 - val_loss: 2.1239 - val_mae: 1.3639\n",
      "Epoch 185/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0345 - mae: 0.7543 - val_loss: 2.1378 - val_mae: 1.3686\n",
      "Epoch 186/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0342 - mae: 0.7504 - val_loss: 2.1561 - val_mae: 1.3749\n",
      "Epoch 187/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0288 - mae: 0.7483 - val_loss: 2.1707 - val_mae: 1.3801\n",
      "Epoch 188/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0227 - mae: 0.7413 - val_loss: 2.1752 - val_mae: 1.3813\n",
      "Epoch 189/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0207 - mae: 0.7402 - val_loss: 2.1793 - val_mae: 1.3824\n",
      "Epoch 190/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0171 - mae: 0.7407 - val_loss: 2.1724 - val_mae: 1.3797\n",
      "Epoch 191/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0127 - mae: 0.7329 - val_loss: 2.1860 - val_mae: 1.3843\n",
      "Epoch 192/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0089 - mae: 0.7266 - val_loss: 2.2038 - val_mae: 1.3903\n",
      "Epoch 193/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0035 - mae: 0.7249 - val_loss: 2.2167 - val_mae: 1.3944\n",
      "Epoch 194/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0034 - mae: 0.7223 - val_loss: 2.2439 - val_mae: 1.4036\n",
      "Epoch 195/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9976 - mae: 0.7229 - val_loss: 2.2558 - val_mae: 1.4073\n",
      "Epoch 196/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9947 - mae: 0.7190 - val_loss: 2.2651 - val_mae: 1.4102\n",
      "Epoch 197/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9922 - mae: 0.7133 - val_loss: 2.2602 - val_mae: 1.4079\n",
      "Epoch 198/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9898 - mae: 0.7086 - val_loss: 2.2721 - val_mae: 1.4115\n",
      "Epoch 199/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9845 - mae: 0.7056 - val_loss: 2.2917 - val_mae: 1.4178\n",
      "Epoch 200/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9829 - mae: 0.7019 - val_loss: 2.2889 - val_mae: 1.4161\n",
      "Epoch 201/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9792 - mae: 0.7007 - val_loss: 2.2945 - val_mae: 1.4175\n",
      "Epoch 202/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9769 - mae: 0.7041 - val_loss: 2.3169 - val_mae: 1.4247\n",
      "Epoch 203/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9721 - mae: 0.6907 - val_loss: 2.3205 - val_mae: 1.4254\n",
      "Epoch 204/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9682 - mae: 0.6925 - val_loss: 2.3289 - val_mae: 1.4278\n",
      "Epoch 205/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9681 - mae: 0.6877 - val_loss: 2.3381 - val_mae: 1.4303\n",
      "Epoch 206/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9632 - mae: 0.6842 - val_loss: 2.3373 - val_mae: 1.4294\n",
      "Epoch 207/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9584 - mae: 0.6803 - val_loss: 2.3361 - val_mae: 1.4283\n",
      "Epoch 208/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9579 - mae: 0.6794 - val_loss: 2.3418 - val_mae: 1.4295\n",
      "Epoch 209/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9537 - mae: 0.6774 - val_loss: 2.3461 - val_mae: 1.4305\n",
      "Epoch 210/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9486 - mae: 0.6730 - val_loss: 2.3426 - val_mae: 1.4284\n",
      "Epoch 211/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9534 - mae: 0.6751 - val_loss: 2.3544 - val_mae: 1.4315\n",
      "Epoch 212/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9444 - mae: 0.6665 - val_loss: 2.3747 - val_mae: 1.4376\n",
      "Epoch 213/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9429 - mae: 0.6638 - val_loss: 2.3820 - val_mae: 1.4396\n",
      "Epoch 214/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9403 - mae: 0.6606 - val_loss: 2.3904 - val_mae: 1.4417\n",
      "Epoch 215/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9365 - mae: 0.6564 - val_loss: 2.3931 - val_mae: 1.4417\n",
      "Epoch 216/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9343 - mae: 0.6517 - val_loss: 2.4016 - val_mae: 1.4435\n",
      "Epoch 217/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9315 - mae: 0.6559 - val_loss: 2.4128 - val_mae: 1.4457\n",
      "Epoch 218/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9300 - mae: 0.6488 - val_loss: 2.4283 - val_mae: 1.4503\n",
      "Epoch 219/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9278 - mae: 0.6448 - val_loss: 2.4212 - val_mae: 1.4467\n",
      "Epoch 220/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9225 - mae: 0.6423 - val_loss: 2.4353 - val_mae: 1.4504\n",
      "Epoch 221/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9194 - mae: 0.6412 - val_loss: 2.4350 - val_mae: 1.4496\n",
      "Epoch 222/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9191 - mae: 0.6424 - val_loss: 2.4457 - val_mae: 1.4519\n",
      "Epoch 223/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9141 - mae: 0.6332 - val_loss: 2.4484 - val_mae: 1.4519\n",
      "Epoch 224/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9125 - mae: 0.6277 - val_loss: 2.4630 - val_mae: 1.4562\n",
      "Epoch 225/500\n",
      "14/14 [==============================] - ETA: 0s - loss: 5.9756 - mae: 2.444 - 0s 4ms/step - loss: 0.9130 - mae: 0.6259 - val_loss: 2.4640 - val_mae: 1.4555\n",
      "Epoch 226/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9084 - mae: 0.6240 - val_loss: 2.4723 - val_mae: 1.4571\n",
      "Epoch 227/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9080 - mae: 0.6235 - val_loss: 2.4708 - val_mae: 1.4556\n",
      "Epoch 228/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9031 - mae: 0.6160 - val_loss: 2.4812 - val_mae: 1.4577\n",
      "Epoch 229/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9037 - mae: 0.6224 - val_loss: 2.4855 - val_mae: 1.4585\n",
      "Epoch 230/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.9036 - mae: 0.6148 - val_loss: 2.4790 - val_mae: 1.4551\n",
      "Epoch 231/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.9012 - mae: 0.6078 - val_loss: 2.4857 - val_mae: 1.4561\n",
      "Epoch 232/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8983 - mae: 0.6162 - val_loss: 2.4837 - val_mae: 1.4549\n",
      "Epoch 233/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.9016 - mae: 0.6051 - val_loss: 2.4941 - val_mae: 1.4569\n",
      "Epoch 234/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8964 - mae: 0.6133 - val_loss: 2.5002 - val_mae: 1.4579\n",
      "Epoch 235/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8935 - mae: 0.6065 - val_loss: 2.5275 - val_mae: 1.4659\n",
      "Epoch 236/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8949 - mae: 0.6039 - val_loss: 2.5376 - val_mae: 1.4684\n",
      "Epoch 237/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8923 - mae: 0.6080 - val_loss: 2.5355 - val_mae: 1.4664\n",
      "Epoch 238/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8911 - mae: 0.6003 - val_loss: 2.5434 - val_mae: 1.4672\n",
      "Epoch 239/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8905 - mae: 0.6016 - val_loss: 2.5372 - val_mae: 1.4644\n",
      "Epoch 240/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8879 - mae: 0.5933 - val_loss: 2.5493 - val_mae: 1.4671\n",
      "Epoch 241/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8879 - mae: 0.6078 - val_loss: 2.5412 - val_mae: 1.4635\n",
      "Epoch 242/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8869 - mae: 0.5944 - val_loss: 2.5497 - val_mae: 1.4651\n",
      "Epoch 243/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8858 - mae: 0.5943 - val_loss: 2.5529 - val_mae: 1.4648\n",
      "Epoch 244/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8826 - mae: 0.6001 - val_loss: 2.5560 - val_mae: 1.4654\n",
      "Epoch 245/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8831 - mae: 0.5914 - val_loss: 2.5606 - val_mae: 1.4655\n",
      "Epoch 246/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8808 - mae: 0.5860 - val_loss: 2.5646 - val_mae: 1.4656\n",
      "Epoch 247/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8802 - mae: 0.5882 - val_loss: 2.5600 - val_mae: 1.4630\n",
      "Epoch 248/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8786 - mae: 0.5810 - val_loss: 2.5636 - val_mae: 1.4629\n",
      "Epoch 249/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8776 - mae: 0.5859 - val_loss: 2.5602 - val_mae: 1.4615\n",
      "Epoch 250/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8762 - mae: 0.5786 - val_loss: 2.5540 - val_mae: 1.4582\n",
      "Epoch 251/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8760 - mae: 0.5809 - val_loss: 2.5723 - val_mae: 1.4621\n",
      "Epoch 252/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8747 - mae: 0.5848 - val_loss: 2.5812 - val_mae: 1.4647\n",
      "Epoch 253/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8751 - mae: 0.5749 - val_loss: 2.5898 - val_mae: 1.4657\n",
      "Epoch 254/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8733 - mae: 0.5775 - val_loss: 2.5917 - val_mae: 1.4660\n",
      "Epoch 255/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8719 - mae: 0.5737 - val_loss: 2.5965 - val_mae: 1.4670\n",
      "Epoch 256/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8719 - mae: 0.5725 - val_loss: 2.6149 - val_mae: 1.4721\n",
      "Epoch 257/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8709 - mae: 0.5688 - val_loss: 2.6249 - val_mae: 1.4735\n",
      "Epoch 258/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8677 - mae: 0.5690 - val_loss: 2.6229 - val_mae: 1.4703\n",
      "Epoch 259/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8687 - mae: 0.5756 - val_loss: 2.6395 - val_mae: 1.4750\n",
      "Epoch 260/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8714 - mae: 0.5651 - val_loss: 2.6453 - val_mae: 1.4744\n",
      "Epoch 261/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8681 - mae: 0.5769 - val_loss: 2.6488 - val_mae: 1.4755\n",
      "Epoch 262/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8669 - mae: 0.5654 - val_loss: 2.6605 - val_mae: 1.4776\n",
      "Epoch 263/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8687 - mae: 0.5630 - val_loss: 2.6628 - val_mae: 1.4758\n",
      "Epoch 264/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8650 - mae: 0.5670 - val_loss: 2.6591 - val_mae: 1.4729\n",
      "Epoch 265/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8659 - mae: 0.5668 - val_loss: 2.6721 - val_mae: 1.4746\n",
      "Epoch 266/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8655 - mae: 0.5689 - val_loss: 2.6862 - val_mae: 1.4783\n",
      "Epoch 267/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8660 - mae: 0.5623 - val_loss: 2.6796 - val_mae: 1.4753\n",
      "Epoch 268/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8634 - mae: 0.5613 - val_loss: 2.7051 - val_mae: 1.4812\n",
      "Epoch 269/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8633 - mae: 0.5688 - val_loss: 2.7097 - val_mae: 1.4815\n",
      "Epoch 270/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8626 - mae: 0.5619 - val_loss: 2.7287 - val_mae: 1.4868\n",
      "Epoch 271/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8654 - mae: 0.5693 - val_loss: 2.7221 - val_mae: 1.4844\n",
      "Epoch 272/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8615 - mae: 0.5604 - val_loss: 2.7257 - val_mae: 1.4857\n",
      "Epoch 273/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8614 - mae: 0.5619 - val_loss: 2.7304 - val_mae: 1.4853\n",
      "Epoch 274/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8612 - mae: 0.5605 - val_loss: 2.7522 - val_mae: 1.4893\n",
      "Epoch 275/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8626 - mae: 0.5603 - val_loss: 2.7414 - val_mae: 1.4850\n",
      "Epoch 276/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8614 - mae: 0.5512 - val_loss: 2.7391 - val_mae: 1.4828\n",
      "Epoch 277/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8603 - mae: 0.5525 - val_loss: 2.7453 - val_mae: 1.4820\n",
      "Epoch 278/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8615 - mae: 0.5498 - val_loss: 2.7499 - val_mae: 1.4807\n",
      "Epoch 279/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8594 - mae: 0.5609 - val_loss: 2.7458 - val_mae: 1.4807\n",
      "Epoch 280/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8578 - mae: 0.5525 - val_loss: 2.7681 - val_mae: 1.4848\n",
      "Epoch 281/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8616 - mae: 0.5571 - val_loss: 2.7492 - val_mae: 1.4791\n",
      "Epoch 282/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8585 - mae: 0.5483 - val_loss: 2.7643 - val_mae: 1.4823\n",
      "Epoch 283/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8596 - mae: 0.5542 - val_loss: 2.7598 - val_mae: 1.4804\n",
      "Epoch 284/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8573 - mae: 0.5534 - val_loss: 2.7811 - val_mae: 1.4846\n",
      "Epoch 285/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8572 - mae: 0.5517 - val_loss: 2.7906 - val_mae: 1.4860\n",
      "Epoch 286/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8580 - mae: 0.5483 - val_loss: 2.7986 - val_mae: 1.4869\n",
      "Epoch 287/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8548 - mae: 0.5472 - val_loss: 2.8037 - val_mae: 1.4876\n",
      "Epoch 288/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8550 - mae: 0.5503 - val_loss: 2.8116 - val_mae: 1.4899\n",
      "Epoch 289/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8558 - mae: 0.5502 - val_loss: 2.8351 - val_mae: 1.4957\n",
      "Epoch 290/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8557 - mae: 0.5465 - val_loss: 2.8401 - val_mae: 1.4960\n",
      "Epoch 291/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8544 - mae: 0.5379 - val_loss: 2.8520 - val_mae: 1.4983\n",
      "Epoch 292/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8527 - mae: 0.5424 - val_loss: 2.8417 - val_mae: 1.4966\n",
      "Epoch 293/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8538 - mae: 0.5422 - val_loss: 2.8727 - val_mae: 1.5038\n",
      "Epoch 294/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8541 - mae: 0.5486 - val_loss: 2.8629 - val_mae: 1.5011\n",
      "Epoch 295/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8525 - mae: 0.5505 - val_loss: 2.8765 - val_mae: 1.5036\n",
      "Epoch 296/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8517 - mae: 0.5421 - val_loss: 2.9048 - val_mae: 1.5110\n",
      "Epoch 297/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8519 - mae: 0.5494 - val_loss: 2.8857 - val_mae: 1.5060\n",
      "Epoch 298/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8524 - mae: 0.5438 - val_loss: 2.9204 - val_mae: 1.5153\n",
      "Epoch 299/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8499 - mae: 0.5418 - val_loss: 2.9196 - val_mae: 1.5148\n",
      "Epoch 300/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8517 - mae: 0.5485 - val_loss: 2.9569 - val_mae: 1.5212\n",
      "Epoch 301/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8528 - mae: 0.5438 - val_loss: 2.9480 - val_mae: 1.5181\n",
      "Epoch 302/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8488 - mae: 0.5410 - val_loss: 2.9807 - val_mae: 1.5256\n",
      "Epoch 303/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8509 - mae: 0.5441 - val_loss: 2.9612 - val_mae: 1.5189\n",
      "Epoch 304/500\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.8488 - mae: 0.5438 - val_loss: 2.9734 - val_mae: 1.5213\n",
      "Epoch 305/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8485 - mae: 0.5461 - val_loss: 2.9829 - val_mae: 1.5234\n",
      "Epoch 306/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8482 - mae: 0.5417 - val_loss: 3.0062 - val_mae: 1.5277\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 307/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8470 - mae: 0.5415 - val_loss: 3.0372 - val_mae: 1.5314\n",
      "Epoch 308/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8477 - mae: 0.5375 - val_loss: 3.0063 - val_mae: 1.5248\n",
      "Epoch 309/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8488 - mae: 0.5459 - val_loss: 3.0386 - val_mae: 1.5321\n",
      "Epoch 310/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8479 - mae: 0.5369 - val_loss: 3.0481 - val_mae: 1.5325\n",
      "Epoch 311/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8486 - mae: 0.5361 - val_loss: 3.0441 - val_mae: 1.5290\n",
      "Epoch 312/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8479 - mae: 0.5381 - val_loss: 3.0759 - val_mae: 1.5360\n",
      "Epoch 313/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8467 - mae: 0.5455 - val_loss: 3.0709 - val_mae: 1.5335\n",
      "Epoch 314/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8460 - mae: 0.5392 - val_loss: 3.0898 - val_mae: 1.5362\n",
      "Epoch 315/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8457 - mae: 0.5336 - val_loss: 3.0798 - val_mae: 1.5329\n",
      "Epoch 316/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8475 - mae: 0.5408 - val_loss: 3.1128 - val_mae: 1.5389\n",
      "Epoch 317/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8467 - mae: 0.5362 - val_loss: 3.1201 - val_mae: 1.5401\n",
      "Epoch 318/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8473 - mae: 0.5368 - val_loss: 3.1367 - val_mae: 1.5425\n",
      "Epoch 319/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8465 - mae: 0.5305 - val_loss: 3.1363 - val_mae: 1.5417\n",
      "Epoch 320/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8456 - mae: 0.5290 - val_loss: 3.1384 - val_mae: 1.5416\n",
      "Epoch 321/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8439 - mae: 0.5381 - val_loss: 3.1369 - val_mae: 1.5415\n",
      "Epoch 322/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8439 - mae: 0.5375 - val_loss: 3.1856 - val_mae: 1.5496\n",
      "Epoch 323/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8444 - mae: 0.5428 - val_loss: 3.1827 - val_mae: 1.5484\n",
      "Epoch 324/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8436 - mae: 0.5369 - val_loss: 3.2072 - val_mae: 1.5523\n",
      "Epoch 325/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8447 - mae: 0.5314 - val_loss: 3.2226 - val_mae: 1.5539\n",
      "Epoch 326/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8437 - mae: 0.5320 - val_loss: 3.2308 - val_mae: 1.5563\n",
      "Epoch 327/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8431 - mae: 0.5284 - val_loss: 3.2433 - val_mae: 1.5586\n",
      "Epoch 328/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8430 - mae: 0.5339 - val_loss: 3.2498 - val_mae: 1.5607\n",
      "Epoch 329/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8445 - mae: 0.5396 - val_loss: 3.3137 - val_mae: 1.5727\n",
      "Epoch 330/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8434 - mae: 0.5312 - val_loss: 3.3087 - val_mae: 1.5706\n",
      "Epoch 331/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8432 - mae: 0.5308 - val_loss: 3.3305 - val_mae: 1.5744\n",
      "Epoch 332/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8411 - mae: 0.5297 - val_loss: 3.3136 - val_mae: 1.5711\n",
      "Epoch 333/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8429 - mae: 0.5374 - val_loss: 3.3708 - val_mae: 1.5805\n",
      "Epoch 334/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8414 - mae: 0.5353 - val_loss: 3.3462 - val_mae: 1.5780\n",
      "Epoch 335/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8408 - mae: 0.5274 - val_loss: 3.3507 - val_mae: 1.5776\n",
      "Epoch 336/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8404 - mae: 0.5346 - val_loss: 3.3720 - val_mae: 1.5833\n",
      "Epoch 337/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8410 - mae: 0.5319 - val_loss: 3.4194 - val_mae: 1.5914\n",
      "Epoch 338/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8397 - mae: 0.5264 - val_loss: 3.4410 - val_mae: 1.5965\n",
      "Epoch 339/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8396 - mae: 0.5288 - val_loss: 3.4629 - val_mae: 1.5997\n",
      "Epoch 340/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8395 - mae: 0.5288 - val_loss: 3.4210 - val_mae: 1.5918\n",
      "Epoch 341/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8423 - mae: 0.5321 - val_loss: 3.4659 - val_mae: 1.5980\n",
      "Epoch 342/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8375 - mae: 0.5235 - val_loss: 3.4085 - val_mae: 1.5876\n",
      "Epoch 343/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8396 - mae: 0.5297 - val_loss: 3.4626 - val_mae: 1.5985\n",
      "Epoch 344/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8403 - mae: 0.5273 - val_loss: 3.4368 - val_mae: 1.5909\n",
      "Epoch 345/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8372 - mae: 0.5297 - val_loss: 3.4792 - val_mae: 1.5964\n",
      "Epoch 346/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8387 - mae: 0.5306 - val_loss: 3.4935 - val_mae: 1.5972\n",
      "Epoch 347/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8387 - mae: 0.5281 - val_loss: 3.4941 - val_mae: 1.5983\n",
      "Epoch 348/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8359 - mae: 0.5284 - val_loss: 3.4597 - val_mae: 1.5889\n",
      "Epoch 349/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8402 - mae: 0.5352 - val_loss: 3.4714 - val_mae: 1.5905\n",
      "Epoch 350/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8375 - mae: 0.5275 - val_loss: 3.4954 - val_mae: 1.5943\n",
      "Epoch 351/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8370 - mae: 0.5331 - val_loss: 3.4843 - val_mae: 1.5935\n",
      "Epoch 352/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8386 - mae: 0.5250 - val_loss: 3.5092 - val_mae: 1.5962\n",
      "Epoch 353/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8383 - mae: 0.5326 - val_loss: 3.5130 - val_mae: 1.5965\n",
      "Epoch 354/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8347 - mae: 0.5221 - val_loss: 3.5199 - val_mae: 1.5946\n",
      "Epoch 355/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8371 - mae: 0.5283 - val_loss: 3.5427 - val_mae: 1.6003\n",
      "Epoch 356/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8355 - mae: 0.5217 - val_loss: 3.5834 - val_mae: 1.6087\n",
      "Epoch 357/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8367 - mae: 0.5258 - val_loss: 3.5941 - val_mae: 1.6106\n",
      "Epoch 358/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8355 - mae: 0.5288 - val_loss: 3.6123 - val_mae: 1.6136\n",
      "Epoch 359/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8362 - mae: 0.5179 - val_loss: 3.6276 - val_mae: 1.6160\n",
      "Epoch 360/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8337 - mae: 0.5239 - val_loss: 3.6203 - val_mae: 1.6127\n",
      "Epoch 361/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8361 - mae: 0.5314 - val_loss: 3.6084 - val_mae: 1.6122\n",
      "Epoch 362/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8335 - mae: 0.5220 - val_loss: 3.6314 - val_mae: 1.6142\n",
      "Epoch 363/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8353 - mae: 0.5253 - val_loss: 3.6146 - val_mae: 1.6129\n",
      "Epoch 364/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8364 - mae: 0.5213 - val_loss: 3.6230 - val_mae: 1.6129\n",
      "Epoch 365/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8337 - mae: 0.5197 - val_loss: 3.6589 - val_mae: 1.6177\n",
      "Epoch 366/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8347 - mae: 0.5198 - val_loss: 3.6524 - val_mae: 1.6159\n",
      "Epoch 367/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8329 - mae: 0.5246 - val_loss: 3.7006 - val_mae: 1.6252\n",
      "Epoch 368/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8325 - mae: 0.5207 - val_loss: 3.6679 - val_mae: 1.6182\n",
      "Epoch 369/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8329 - mae: 0.5172 - val_loss: 3.6934 - val_mae: 1.6218\n",
      "Epoch 370/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8335 - mae: 0.5237 - val_loss: 3.6301 - val_mae: 1.6124\n",
      "Epoch 371/500\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.8330 - mae: 0.5280 - val_loss: 3.6727 - val_mae: 1.6210\n",
      "Epoch 372/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8323 - mae: 0.5253 - val_loss: 3.6899 - val_mae: 1.6242\n",
      "Epoch 373/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8318 - mae: 0.5190 - val_loss: 3.7236 - val_mae: 1.6299\n",
      "Epoch 374/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8322 - mae: 0.5148 - val_loss: 3.7456 - val_mae: 1.6332\n",
      "Epoch 375/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8338 - mae: 0.5238 - val_loss: 3.7643 - val_mae: 1.6338\n",
      "Epoch 376/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8317 - mae: 0.5109 - val_loss: 3.7275 - val_mae: 1.6282\n",
      "Epoch 377/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8303 - mae: 0.5187 - val_loss: 3.7281 - val_mae: 1.6274\n",
      "Epoch 378/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8309 - mae: 0.5190 - val_loss: 3.7253 - val_mae: 1.6285\n",
      "Epoch 379/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8303 - mae: 0.5209 - val_loss: 3.7695 - val_mae: 1.6358\n",
      "Epoch 380/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8323 - mae: 0.5160 - val_loss: 3.7919 - val_mae: 1.6364\n",
      "Epoch 381/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8289 - mae: 0.5112 - val_loss: 3.8194 - val_mae: 1.6432\n",
      "Epoch 382/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8310 - mae: 0.5233 - val_loss: 3.7841 - val_mae: 1.6369\n",
      "Epoch 383/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8297 - mae: 0.5146 - val_loss: 3.8192 - val_mae: 1.6457\n",
      "Epoch 384/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8275 - mae: 0.5159 - val_loss: 3.8428 - val_mae: 1.6497\n",
      "Epoch 385/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8283 - mae: 0.5225 - val_loss: 3.8555 - val_mae: 1.6503\n",
      "Epoch 386/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8269 - mae: 0.5151 - val_loss: 3.8239 - val_mae: 1.6445\n",
      "Epoch 387/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8277 - mae: 0.5134 - val_loss: 3.8730 - val_mae: 1.6511\n",
      "Epoch 388/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8285 - mae: 0.5196 - val_loss: 3.8528 - val_mae: 1.6505\n",
      "Epoch 389/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8275 - mae: 0.5118 - val_loss: 3.9044 - val_mae: 1.6569\n",
      "Epoch 390/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8293 - mae: 0.5143 - val_loss: 3.9007 - val_mae: 1.6553\n",
      "Epoch 391/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8285 - mae: 0.5133 - val_loss: 3.8976 - val_mae: 1.6533\n",
      "Epoch 392/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8259 - mae: 0.5077 - val_loss: 3.9225 - val_mae: 1.6552\n",
      "Epoch 393/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8273 - mae: 0.5139 - val_loss: 3.7987 - val_mae: 1.6359\n",
      "Epoch 394/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8268 - mae: 0.5121 - val_loss: 3.8789 - val_mae: 1.6492\n",
      "Epoch 395/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8277 - mae: 0.5220 - val_loss: 3.8321 - val_mae: 1.6443\n",
      "Epoch 396/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8257 - mae: 0.5158 - val_loss: 3.9051 - val_mae: 1.6552\n",
      "Epoch 397/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8256 - mae: 0.5100 - val_loss: 3.9203 - val_mae: 1.6571\n",
      "Epoch 398/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8268 - mae: 0.5191 - val_loss: 3.9376 - val_mae: 1.6584\n",
      "Epoch 399/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8240 - mae: 0.5061 - val_loss: 3.9273 - val_mae: 1.6552\n",
      "Epoch 400/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8266 - mae: 0.5191 - val_loss: 3.9072 - val_mae: 1.6528\n",
      "Epoch 401/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8252 - mae: 0.5112 - val_loss: 3.9251 - val_mae: 1.6577\n",
      "Epoch 402/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8251 - mae: 0.5184 - val_loss: 3.9412 - val_mae: 1.6615\n",
      "Epoch 403/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8240 - mae: 0.5077 - val_loss: 3.9696 - val_mae: 1.6652\n",
      "Epoch 404/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8237 - mae: 0.5143 - val_loss: 3.9899 - val_mae: 1.6677\n",
      "Epoch 405/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8244 - mae: 0.5075 - val_loss: 4.0086 - val_mae: 1.6687\n",
      "Epoch 406/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8253 - mae: 0.5111 - val_loss: 3.9964 - val_mae: 1.6673\n",
      "Epoch 407/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8228 - mae: 0.5145 - val_loss: 4.0467 - val_mae: 1.6723\n",
      "Epoch 408/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8229 - mae: 0.5060 - val_loss: 3.9758 - val_mae: 1.6635\n",
      "Epoch 409/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8232 - mae: 0.5132 - val_loss: 4.0391 - val_mae: 1.6737\n",
      "Epoch 410/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8238 - mae: 0.5095 - val_loss: 4.0095 - val_mae: 1.6698\n",
      "Epoch 411/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8240 - mae: 0.5147 - val_loss: 4.1145 - val_mae: 1.6868\n",
      "Epoch 412/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8208 - mae: 0.5049 - val_loss: 4.0942 - val_mae: 1.6835\n",
      "Epoch 413/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8230 - mae: 0.5030 - val_loss: 4.1341 - val_mae: 1.6902\n",
      "Epoch 414/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8212 - mae: 0.5164 - val_loss: 4.1013 - val_mae: 1.6834\n",
      "Epoch 415/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8229 - mae: 0.5012 - val_loss: 4.1430 - val_mae: 1.6892\n",
      "Epoch 416/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8216 - mae: 0.5108 - val_loss: 4.1146 - val_mae: 1.6852\n",
      "Epoch 417/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8221 - mae: 0.5049 - val_loss: 4.1180 - val_mae: 1.6845\n",
      "Epoch 418/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8202 - mae: 0.5029 - val_loss: 4.1639 - val_mae: 1.6922\n",
      "Epoch 419/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8219 - mae: 0.5036 - val_loss: 4.1712 - val_mae: 1.6935\n",
      "Epoch 420/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8217 - mae: 0.5137 - val_loss: 4.1653 - val_mae: 1.6931\n",
      "Epoch 421/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8195 - mae: 0.5006 - val_loss: 4.1918 - val_mae: 1.6959\n",
      "Epoch 422/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8209 - mae: 0.5059 - val_loss: 4.1869 - val_mae: 1.6964\n",
      "Epoch 423/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8210 - mae: 0.5062 - val_loss: 4.2509 - val_mae: 1.7047\n",
      "Epoch 424/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8196 - mae: 0.5099 - val_loss: 4.2692 - val_mae: 1.7103\n",
      "Epoch 425/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8190 - mae: 0.5024 - val_loss: 4.2847 - val_mae: 1.7124\n",
      "Epoch 426/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8216 - mae: 0.5118 - val_loss: 4.2730 - val_mae: 1.7098\n",
      "Epoch 427/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8179 - mae: 0.4967 - val_loss: 4.3012 - val_mae: 1.7133\n",
      "Epoch 428/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8195 - mae: 0.5090 - val_loss: 4.2984 - val_mae: 1.7153\n",
      "Epoch 429/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8181 - mae: 0.5030 - val_loss: 4.3877 - val_mae: 1.7280\n",
      "Epoch 430/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8187 - mae: 0.5036 - val_loss: 4.2502 - val_mae: 1.7072\n",
      "Epoch 431/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8180 - mae: 0.5061 - val_loss: 4.3089 - val_mae: 1.7155\n",
      "Epoch 432/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8178 - mae: 0.4966 - val_loss: 4.3155 - val_mae: 1.7157\n",
      "Epoch 433/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8181 - mae: 0.5014 - val_loss: 4.3648 - val_mae: 1.7237\n",
      "Epoch 434/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8178 - mae: 0.5046 - val_loss: 4.2339 - val_mae: 1.7089\n",
      "Epoch 435/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8169 - mae: 0.5008 - val_loss: 4.2823 - val_mae: 1.7153\n",
      "Epoch 436/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8185 - mae: 0.5121 - val_loss: 4.3680 - val_mae: 1.7293\n",
      "Epoch 437/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8165 - mae: 0.4954 - val_loss: 4.3218 - val_mae: 1.7198\n",
      "Epoch 438/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8162 - mae: 0.4976 - val_loss: 4.3989 - val_mae: 1.7325\n",
      "Epoch 439/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8166 - mae: 0.4958 - val_loss: 4.4242 - val_mae: 1.7358\n",
      "Epoch 440/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8154 - mae: 0.4958 - val_loss: 4.3971 - val_mae: 1.7321\n",
      "Epoch 441/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8169 - mae: 0.5023 - val_loss: 4.3785 - val_mae: 1.7303\n",
      "Epoch 442/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8147 - mae: 0.4982 - val_loss: 4.4275 - val_mae: 1.7385\n",
      "Epoch 443/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8151 - mae: 0.4955 - val_loss: 4.4058 - val_mae: 1.7353\n",
      "Epoch 444/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8142 - mae: 0.4942 - val_loss: 4.3789 - val_mae: 1.7272\n",
      "Epoch 445/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8188 - mae: 0.5005 - val_loss: 4.3271 - val_mae: 1.7175\n",
      "Epoch 446/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8150 - mae: 0.4981 - val_loss: 4.3940 - val_mae: 1.7284\n",
      "Epoch 447/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8139 - mae: 0.4931 - val_loss: 4.3230 - val_mae: 1.7170\n",
      "Epoch 448/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8161 - mae: 0.4978 - val_loss: 4.4101 - val_mae: 1.7299\n",
      "Epoch 449/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8140 - mae: 0.4923 - val_loss: 4.3977 - val_mae: 1.7301\n",
      "Epoch 450/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8128 - mae: 0.4877 - val_loss: 4.3563 - val_mae: 1.7240\n",
      "Epoch 451/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8130 - mae: 0.4890 - val_loss: 4.4047 - val_mae: 1.7285\n",
      "Epoch 452/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8154 - mae: 0.4936 - val_loss: 4.3219 - val_mae: 1.7173\n",
      "Epoch 453/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8136 - mae: 0.4916 - val_loss: 4.3937 - val_mae: 1.7290\n",
      "Epoch 454/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8123 - mae: 0.4848 - val_loss: 4.3597 - val_mae: 1.7237\n",
      "Epoch 455/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8127 - mae: 0.4896 - val_loss: 4.3966 - val_mae: 1.7313\n",
      "Epoch 456/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8136 - mae: 0.5003 - val_loss: 4.3684 - val_mae: 1.7269\n",
      "Epoch 457/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8128 - mae: 0.4853 - val_loss: 4.3692 - val_mae: 1.7269\n",
      "Epoch 458/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8131 - mae: 0.4876 - val_loss: 4.3203 - val_mae: 1.7200\n",
      "Epoch 459/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8125 - mae: 0.4968 - val_loss: 4.2699 - val_mae: 1.7158\n",
      "Epoch 460/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8108 - mae: 0.4913 - val_loss: 4.3821 - val_mae: 1.7342\n",
      "Epoch 461/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8110 - mae: 0.4909 - val_loss: 4.3033 - val_mae: 1.7234\n",
      "Epoch 462/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8115 - mae: 0.4920 - val_loss: 4.3933 - val_mae: 1.7325\n",
      "Epoch 463/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8136 - mae: 0.4924 - val_loss: 4.3525 - val_mae: 1.7261\n",
      "Epoch 464/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8105 - mae: 0.4859 - val_loss: 4.4008 - val_mae: 1.7346\n",
      "Epoch 465/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8121 - mae: 0.4885 - val_loss: 4.3946 - val_mae: 1.7317\n",
      "Epoch 466/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8108 - mae: 0.4934 - val_loss: 4.4432 - val_mae: 1.7372\n",
      "Epoch 467/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8124 - mae: 0.4834 - val_loss: 4.4149 - val_mae: 1.7315\n",
      "Epoch 468/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8108 - mae: 0.4914 - val_loss: 4.3628 - val_mae: 1.7247\n",
      "Epoch 469/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8126 - mae: 0.4970 - val_loss: 4.3589 - val_mae: 1.7242\n",
      "Epoch 470/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8105 - mae: 0.4847 - val_loss: 4.3889 - val_mae: 1.7274\n",
      "Epoch 471/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8116 - mae: 0.4902 - val_loss: 4.2443 - val_mae: 1.7073\n",
      "Epoch 472/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8126 - mae: 0.4872 - val_loss: 4.3265 - val_mae: 1.7143\n",
      "Epoch 473/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8102 - mae: 0.4861 - val_loss: 4.3599 - val_mae: 1.7211\n",
      "Epoch 474/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8089 - mae: 0.4844 - val_loss: 4.4009 - val_mae: 1.7273\n",
      "Epoch 475/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8114 - mae: 0.4929 - val_loss: 4.3636 - val_mae: 1.7215\n",
      "Epoch 476/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8097 - mae: 0.4775 - val_loss: 4.3452 - val_mae: 1.7189\n",
      "Epoch 477/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8106 - mae: 0.4825 - val_loss: 4.3442 - val_mae: 1.7197\n",
      "Epoch 478/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8104 - mae: 0.4828 - val_loss: 4.3407 - val_mae: 1.7194\n",
      "Epoch 479/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8116 - mae: 0.4808 - val_loss: 4.3028 - val_mae: 1.7109\n",
      "Epoch 480/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8101 - mae: 0.4847 - val_loss: 4.3760 - val_mae: 1.7208\n",
      "Epoch 481/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8089 - mae: 0.4804 - val_loss: 4.4166 - val_mae: 1.7259\n",
      "Epoch 482/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8090 - mae: 0.4831 - val_loss: 4.4038 - val_mae: 1.7280\n",
      "Epoch 483/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8098 - mae: 0.4778 - val_loss: 4.3950 - val_mae: 1.7275\n",
      "Epoch 484/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8085 - mae: 0.4798 - val_loss: 4.4009 - val_mae: 1.7283\n",
      "Epoch 485/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8091 - mae: 0.4780 - val_loss: 4.4507 - val_mae: 1.7369\n",
      "Epoch 486/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8081 - mae: 0.4889 - val_loss: 4.4774 - val_mae: 1.7393\n",
      "Epoch 487/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8100 - mae: 0.4848 - val_loss: 4.4594 - val_mae: 1.7397\n",
      "Epoch 488/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8096 - mae: 0.4740 - val_loss: 4.3608 - val_mae: 1.7250\n",
      "Epoch 489/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8076 - mae: 0.4754 - val_loss: 4.4314 - val_mae: 1.7338\n",
      "Epoch 490/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8096 - mae: 0.4737 - val_loss: 4.3950 - val_mae: 1.7258\n",
      "Epoch 491/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8118 - mae: 0.4871 - val_loss: 4.4195 - val_mae: 1.7291\n",
      "Epoch 492/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8092 - mae: 0.4849 - val_loss: 4.3993 - val_mae: 1.7283\n",
      "Epoch 493/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8076 - mae: 0.4698 - val_loss: 4.4673 - val_mae: 1.7377\n",
      "Epoch 494/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8090 - mae: 0.4698 - val_loss: 4.4811 - val_mae: 1.7392\n",
      "Epoch 495/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8081 - mae: 0.4721 - val_loss: 4.3900 - val_mae: 1.7282\n",
      "Epoch 496/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8074 - mae: 0.4763 - val_loss: 4.3802 - val_mae: 1.7262\n",
      "Epoch 497/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8084 - mae: 0.4700 - val_loss: 4.3828 - val_mae: 1.7292\n",
      "Epoch 498/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8094 - mae: 0.4862 - val_loss: 4.4558 - val_mae: 1.7394\n",
      "Epoch 499/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8077 - mae: 0.4705 - val_loss: 4.4304 - val_mae: 1.7365\n",
      "Epoch 500/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8064 - mae: 0.4725 - val_loss: 4.4245 - val_mae: 1.7341\n",
      "processing fold # 1\n",
      "Epoch 1/500\n",
      "14/14 [==============================] - 0s 16ms/step - loss: 7.5624 - mae: 2.2277 - val_loss: 3.0022 - val_mae: 1.5101\n",
      "Epoch 2/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 7.3525 - mae: 2.1811 - val_loss: 2.9481 - val_mae: 1.4894\n",
      "Epoch 3/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 7.1839 - mae: 2.1507 - val_loss: 2.8977 - val_mae: 1.4698\n",
      "Epoch 4/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 7.0251 - mae: 2.1217 - val_loss: 2.8501 - val_mae: 1.4514\n",
      "Epoch 5/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.8789 - mae: 2.0910 - val_loss: 2.8091 - val_mae: 1.4350\n",
      "Epoch 6/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.7379 - mae: 2.0650 - val_loss: 2.7779 - val_mae: 1.4240\n",
      "Epoch 7/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.5955 - mae: 2.0416 - val_loss: 2.7459 - val_mae: 1.4128\n",
      "Epoch 8/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.4575 - mae: 2.0145 - val_loss: 2.7158 - val_mae: 1.4021\n",
      "Epoch 9/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.3227 - mae: 1.9883 - val_loss: 2.6864 - val_mae: 1.3915\n",
      "Epoch 10/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.1944 - mae: 1.9629 - val_loss: 2.6572 - val_mae: 1.3810\n",
      "Epoch 11/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 6.0606 - mae: 1.9410 - val_loss: 2.6254 - val_mae: 1.3695\n",
      "Epoch 12/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.9309 - mae: 1.9100 - val_loss: 2.5984 - val_mae: 1.3596\n",
      "Epoch 13/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.8034 - mae: 1.8873 - val_loss: 2.5680 - val_mae: 1.3483\n",
      "Epoch 14/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.6747 - mae: 1.8569 - val_loss: 2.5386 - val_mae: 1.3374\n",
      "Epoch 15/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.5568 - mae: 1.8305 - val_loss: 2.5116 - val_mae: 1.3273\n",
      "Epoch 16/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.4373 - mae: 1.8022 - val_loss: 2.4846 - val_mae: 1.3170\n",
      "Epoch 17/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.3152 - mae: 1.7765 - val_loss: 2.4548 - val_mae: 1.3057\n",
      "Epoch 18/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.2021 - mae: 1.7451 - val_loss: 2.4301 - val_mae: 1.2962\n",
      "Epoch 19/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.0883 - mae: 1.7204 - val_loss: 2.4036 - val_mae: 1.2859\n",
      "Epoch 20/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.9765 - mae: 1.6952 - val_loss: 2.3762 - val_mae: 1.2752\n",
      "Epoch 21/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.8559 - mae: 1.6736 - val_loss: 2.3485 - val_mae: 1.2643\n",
      "Epoch 22/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.7516 - mae: 1.6537 - val_loss: 2.3241 - val_mae: 1.2546\n",
      "Epoch 23/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.6476 - mae: 1.6333 - val_loss: 2.2975 - val_mae: 1.2440\n",
      "Epoch 24/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.5467 - mae: 1.6115 - val_loss: 2.2739 - val_mae: 1.2345\n",
      "Epoch 25/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.4454 - mae: 1.5861 - val_loss: 2.2501 - val_mae: 1.2248\n",
      "Epoch 26/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.3475 - mae: 1.5704 - val_loss: 2.2248 - val_mae: 1.2144\n",
      "Epoch 27/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.2525 - mae: 1.5510 - val_loss: 2.2022 - val_mae: 1.2051\n",
      "Epoch 28/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1657 - mae: 1.5315 - val_loss: 2.1806 - val_mae: 1.1961\n",
      "Epoch 29/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.0729 - mae: 1.5165 - val_loss: 2.1557 - val_mae: 1.1856\n",
      "Epoch 30/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9806 - mae: 1.4983 - val_loss: 2.1341 - val_mae: 1.1765\n",
      "Epoch 31/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8947 - mae: 1.4846 - val_loss: 2.1127 - val_mae: 1.1673\n",
      "Epoch 32/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8146 - mae: 1.4697 - val_loss: 2.0897 - val_mae: 1.1574\n",
      "Epoch 33/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.7276 - mae: 1.4546 - val_loss: 2.0695 - val_mae: 1.1487\n",
      "Epoch 34/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.6473 - mae: 1.4367 - val_loss: 2.0463 - val_mae: 1.1385\n",
      "Epoch 35/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.5603 - mae: 1.4167 - val_loss: 2.0245 - val_mae: 1.1289\n",
      "Epoch 36/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4848 - mae: 1.3950 - val_loss: 2.0063 - val_mae: 1.1208\n",
      "Epoch 37/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4014 - mae: 1.3768 - val_loss: 1.9829 - val_mae: 1.1092\n",
      "Epoch 38/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3271 - mae: 1.3548 - val_loss: 1.9576 - val_mae: 1.0953\n",
      "Epoch 39/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2600 - mae: 1.3356 - val_loss: 1.9340 - val_mae: 1.0814\n",
      "Epoch 40/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1985 - mae: 1.3181 - val_loss: 1.9098 - val_mae: 1.0672\n",
      "Epoch 41/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1361 - mae: 1.2985 - val_loss: 1.8861 - val_mae: 1.0526\n",
      "Epoch 42/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0747 - mae: 1.2780 - val_loss: 1.8650 - val_mae: 1.0400\n",
      "Epoch 43/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0205 - mae: 1.2630 - val_loss: 1.8415 - val_mae: 1.0248\n",
      "Epoch 44/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9671 - mae: 1.2379 - val_loss: 1.8212 - val_mae: 1.0114\n",
      "Epoch 45/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9184 - mae: 1.2206 - val_loss: 1.8027 - val_mae: 0.9994\n",
      "Epoch 46/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8758 - mae: 1.2027 - val_loss: 1.7832 - val_mae: 0.9872\n",
      "Epoch 47/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8332 - mae: 1.1844 - val_loss: 1.7377 - val_mae: 0.9708\n",
      "Epoch 48/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7997 - mae: 1.1636 - val_loss: 1.6957 - val_mae: 0.9548\n",
      "Epoch 49/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7657 - mae: 1.1466 - val_loss: 1.6523 - val_mae: 0.9378\n",
      "Epoch 50/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7346 - mae: 1.1368 - val_loss: 1.6115 - val_mae: 0.9221\n",
      "Epoch 51/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.7087 - mae: 1.1249 - val_loss: 1.5713 - val_mae: 0.9076\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 52/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.6843 - mae: 1.1209 - val_loss: 1.5326 - val_mae: 0.8929\n",
      "Epoch 53/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.6547 - mae: 1.1119 - val_loss: 1.5000 - val_mae: 0.8796\n",
      "Epoch 54/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.6356 - mae: 1.1107 - val_loss: 1.4658 - val_mae: 0.8672\n",
      "Epoch 55/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.6159 - mae: 1.1035 - val_loss: 1.4327 - val_mae: 0.8548\n",
      "Epoch 56/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.5931 - mae: 1.1043 - val_loss: 1.4040 - val_mae: 0.8435\n",
      "Epoch 57/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.5783 - mae: 1.0944 - val_loss: 1.3729 - val_mae: 0.8331\n",
      "Epoch 58/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5590 - mae: 1.0948 - val_loss: 1.3436 - val_mae: 0.8214\n",
      "Epoch 59/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5425 - mae: 1.0928 - val_loss: 1.3200 - val_mae: 0.8124\n",
      "Epoch 60/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5289 - mae: 1.0908 - val_loss: 1.3037 - val_mae: 0.8063\n",
      "Epoch 61/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5129 - mae: 1.0881 - val_loss: 1.2881 - val_mae: 0.7996\n",
      "Epoch 62/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4979 - mae: 1.0860 - val_loss: 1.2739 - val_mae: 0.7952\n",
      "Epoch 63/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4897 - mae: 1.0812 - val_loss: 1.2526 - val_mae: 0.7892\n",
      "Epoch 64/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4744 - mae: 1.0775 - val_loss: 1.2400 - val_mae: 0.7838\n",
      "Epoch 65/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4635 - mae: 1.0745 - val_loss: 1.2320 - val_mae: 0.7826\n",
      "Epoch 66/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4565 - mae: 1.0702 - val_loss: 1.2207 - val_mae: 0.7786\n",
      "Epoch 67/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4453 - mae: 1.0618 - val_loss: 1.1998 - val_mae: 0.7734\n",
      "Epoch 68/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4355 - mae: 1.0632 - val_loss: 1.1891 - val_mae: 0.7685\n",
      "Epoch 69/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4265 - mae: 1.0560 - val_loss: 1.1762 - val_mae: 0.7634\n",
      "Epoch 70/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4136 - mae: 1.0533 - val_loss: 1.1664 - val_mae: 0.7594\n",
      "Epoch 71/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4067 - mae: 1.0510 - val_loss: 1.1500 - val_mae: 0.7564\n",
      "Epoch 72/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.3942 - mae: 1.0467 - val_loss: 1.1390 - val_mae: 0.7516\n",
      "Epoch 73/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3858 - mae: 1.0406 - val_loss: 1.1298 - val_mae: 0.7493\n",
      "Epoch 74/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.3748 - mae: 1.0358 - val_loss: 1.1216 - val_mae: 0.7463\n",
      "Epoch 75/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.3624 - mae: 1.0325 - val_loss: 1.1195 - val_mae: 0.7481\n",
      "Epoch 76/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3584 - mae: 1.0250 - val_loss: 1.1117 - val_mae: 0.7457\n",
      "Epoch 77/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3476 - mae: 1.0215 - val_loss: 1.0937 - val_mae: 0.7395\n",
      "Epoch 78/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3380 - mae: 1.0192 - val_loss: 1.0857 - val_mae: 0.7370\n",
      "Epoch 79/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3306 - mae: 1.0152 - val_loss: 1.0799 - val_mae: 0.7350\n",
      "Epoch 80/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3215 - mae: 1.0122 - val_loss: 1.0722 - val_mae: 0.7320\n",
      "Epoch 81/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3129 - mae: 1.0047 - val_loss: 1.0611 - val_mae: 0.7277\n",
      "Epoch 82/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3051 - mae: 1.0025 - val_loss: 1.0514 - val_mae: 0.7226\n",
      "Epoch 83/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2951 - mae: 0.9991 - val_loss: 1.0483 - val_mae: 0.7232\n",
      "Epoch 84/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2900 - mae: 0.9966 - val_loss: 1.0316 - val_mae: 0.7175\n",
      "Epoch 85/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2800 - mae: 0.9965 - val_loss: 1.0211 - val_mae: 0.7121\n",
      "Epoch 86/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2737 - mae: 0.9921 - val_loss: 1.0123 - val_mae: 0.7075\n",
      "Epoch 87/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2652 - mae: 0.9902 - val_loss: 1.0067 - val_mae: 0.7054\n",
      "Epoch 88/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2593 - mae: 0.9871 - val_loss: 0.9992 - val_mae: 0.7021\n",
      "Epoch 89/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2487 - mae: 0.9832 - val_loss: 0.9906 - val_mae: 0.6975\n",
      "Epoch 90/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2453 - mae: 0.9815 - val_loss: 0.9858 - val_mae: 0.6960\n",
      "Epoch 91/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2373 - mae: 0.9809 - val_loss: 0.9791 - val_mae: 0.6921\n",
      "Epoch 92/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2300 - mae: 0.9769 - val_loss: 0.9741 - val_mae: 0.6902\n",
      "Epoch 93/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2216 - mae: 0.9751 - val_loss: 0.9676 - val_mae: 0.6865\n",
      "Epoch 94/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2135 - mae: 0.9711 - val_loss: 0.9644 - val_mae: 0.6862\n",
      "Epoch 95/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2094 - mae: 0.9693 - val_loss: 0.9600 - val_mae: 0.6834\n",
      "Epoch 96/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2053 - mae: 0.9633 - val_loss: 0.9510 - val_mae: 0.6783\n",
      "Epoch 97/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1951 - mae: 0.9607 - val_loss: 0.9437 - val_mae: 0.6742\n",
      "Epoch 98/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1912 - mae: 0.9608 - val_loss: 0.9387 - val_mae: 0.6721\n",
      "Epoch 99/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1818 - mae: 0.9577 - val_loss: 0.9257 - val_mae: 0.6673\n",
      "Epoch 100/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1778 - mae: 0.9554 - val_loss: 0.9178 - val_mae: 0.6626\n",
      "Epoch 101/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1694 - mae: 0.9521 - val_loss: 0.9125 - val_mae: 0.6592\n",
      "Epoch 102/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1629 - mae: 0.9476 - val_loss: 0.9077 - val_mae: 0.6574\n",
      "Epoch 103/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1565 - mae: 0.9443 - val_loss: 0.9036 - val_mae: 0.6549\n",
      "Epoch 104/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1487 - mae: 0.9415 - val_loss: 0.8998 - val_mae: 0.6523\n",
      "Epoch 105/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1427 - mae: 0.9412 - val_loss: 0.8890 - val_mae: 0.6467\n",
      "Epoch 106/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1356 - mae: 0.9383 - val_loss: 0.8860 - val_mae: 0.6446\n",
      "Epoch 107/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1269 - mae: 0.9357 - val_loss: 0.8838 - val_mae: 0.6426\n",
      "Epoch 108/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1236 - mae: 0.9321 - val_loss: 0.8759 - val_mae: 0.6377\n",
      "Epoch 109/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1158 - mae: 0.9302 - val_loss: 0.8685 - val_mae: 0.6334\n",
      "Epoch 110/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1114 - mae: 0.9280 - val_loss: 0.8608 - val_mae: 0.6286\n",
      "Epoch 111/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1033 - mae: 0.9229 - val_loss: 0.8550 - val_mae: 0.6246\n",
      "Epoch 112/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0955 - mae: 0.9225 - val_loss: 0.8509 - val_mae: 0.6212\n",
      "Epoch 113/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0916 - mae: 0.9208 - val_loss: 0.8471 - val_mae: 0.6176\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 114/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0858 - mae: 0.9128 - val_loss: 0.8419 - val_mae: 0.6135\n",
      "Epoch 115/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0781 - mae: 0.9097 - val_loss: 0.8370 - val_mae: 0.6098\n",
      "Epoch 116/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0726 - mae: 0.9111 - val_loss: 0.8327 - val_mae: 0.6060\n",
      "Epoch 117/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0651 - mae: 0.9100 - val_loss: 0.8285 - val_mae: 0.6027\n",
      "Epoch 118/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0563 - mae: 0.9026 - val_loss: 0.8234 - val_mae: 0.5984\n",
      "Epoch 119/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0543 - mae: 0.9041 - val_loss: 0.8183 - val_mae: 0.5945\n",
      "Epoch 120/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0472 - mae: 0.9002 - val_loss: 0.8135 - val_mae: 0.5911\n",
      "Epoch 121/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0397 - mae: 0.8990 - val_loss: 0.8097 - val_mae: 0.5877\n",
      "Epoch 122/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0366 - mae: 0.8932 - val_loss: 0.8038 - val_mae: 0.5834\n",
      "Epoch 123/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0305 - mae: 0.8941 - val_loss: 0.8010 - val_mae: 0.5802\n",
      "Epoch 124/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0212 - mae: 0.8898 - val_loss: 0.7983 - val_mae: 0.5772\n",
      "Epoch 125/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0166 - mae: 0.8917 - val_loss: 0.7941 - val_mae: 0.5731\n",
      "Epoch 126/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0130 - mae: 0.8861 - val_loss: 0.7923 - val_mae: 0.5702\n",
      "Epoch 127/500\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 1.9976 - mae: 0.8827 - val_loss: 0.7902 - val_mae: 0.5672\n",
      "Epoch 128/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9886 - mae: 0.8800 - val_loss: 0.7880 - val_mae: 0.5644\n",
      "Epoch 129/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9798 - mae: 0.8753 - val_loss: 0.7837 - val_mae: 0.5610\n",
      "Epoch 130/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9692 - mae: 0.8742 - val_loss: 0.7806 - val_mae: 0.5571\n",
      "Epoch 131/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9603 - mae: 0.8676 - val_loss: 0.7767 - val_mae: 0.5538\n",
      "Epoch 132/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9516 - mae: 0.8680 - val_loss: 0.7707 - val_mae: 0.5493\n",
      "Epoch 133/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9430 - mae: 0.8649 - val_loss: 0.7676 - val_mae: 0.5457\n",
      "Epoch 134/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9311 - mae: 0.8614 - val_loss: 0.7631 - val_mae: 0.5424\n",
      "Epoch 135/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9244 - mae: 0.8592 - val_loss: 0.7599 - val_mae: 0.5382\n",
      "Epoch 136/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9147 - mae: 0.8535 - val_loss: 0.7545 - val_mae: 0.5340\n",
      "Epoch 137/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.8996 - mae: 0.8535 - val_loss: 0.7493 - val_mae: 0.5295\n",
      "Epoch 138/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8897 - mae: 0.8492 - val_loss: 0.7460 - val_mae: 0.5257\n",
      "Epoch 139/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8757 - mae: 0.8479 - val_loss: 0.7455 - val_mae: 0.5229\n",
      "Epoch 140/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8617 - mae: 0.8431 - val_loss: 0.7428 - val_mae: 0.5196\n",
      "Epoch 141/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.8536 - mae: 0.8353 - val_loss: 0.7412 - val_mae: 0.5164\n",
      "Epoch 142/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8398 - mae: 0.8334 - val_loss: 0.7412 - val_mae: 0.5134\n",
      "Epoch 143/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.8292 - mae: 0.8254 - val_loss: 0.7370 - val_mae: 0.5095\n",
      "Epoch 144/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8182 - mae: 0.8225 - val_loss: 0.7353 - val_mae: 0.5063\n",
      "Epoch 145/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.8034 - mae: 0.8204 - val_loss: 0.7336 - val_mae: 0.5033\n",
      "Epoch 146/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7940 - mae: 0.8173 - val_loss: 0.7310 - val_mae: 0.4995\n",
      "Epoch 147/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7828 - mae: 0.8107 - val_loss: 0.7317 - val_mae: 0.4969\n",
      "Epoch 148/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7723 - mae: 0.8029 - val_loss: 0.7302 - val_mae: 0.4935\n",
      "Epoch 149/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7593 - mae: 0.7975 - val_loss: 0.7303 - val_mae: 0.4905\n",
      "Epoch 150/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7463 - mae: 0.7905 - val_loss: 0.7280 - val_mae: 0.4869\n",
      "Epoch 151/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7326 - mae: 0.7910 - val_loss: 0.7293 - val_mae: 0.4843\n",
      "Epoch 152/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7245 - mae: 0.7832 - val_loss: 0.7280 - val_mae: 0.4810\n",
      "Epoch 153/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7158 - mae: 0.7743 - val_loss: 0.7257 - val_mae: 0.4775\n",
      "Epoch 154/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.7049 - mae: 0.7696 - val_loss: 0.7267 - val_mae: 0.4743\n",
      "Epoch 155/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6924 - mae: 0.7643 - val_loss: 0.7235 - val_mae: 0.4703\n",
      "Epoch 156/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6825 - mae: 0.7593 - val_loss: 0.7231 - val_mae: 0.4674\n",
      "Epoch 157/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6748 - mae: 0.7573 - val_loss: 0.7235 - val_mae: 0.4647\n",
      "Epoch 158/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6595 - mae: 0.7552 - val_loss: 0.7248 - val_mae: 0.4622\n",
      "Epoch 159/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6515 - mae: 0.7499 - val_loss: 0.7234 - val_mae: 0.4589\n",
      "Epoch 160/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6462 - mae: 0.7424 - val_loss: 0.7258 - val_mae: 0.4567\n",
      "Epoch 161/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6416 - mae: 0.7384 - val_loss: 0.7221 - val_mae: 0.4528\n",
      "Epoch 162/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6305 - mae: 0.7391 - val_loss: 0.7155 - val_mae: 0.4475\n",
      "Epoch 163/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6200 - mae: 0.7344 - val_loss: 0.7169 - val_mae: 0.4454\n",
      "Epoch 164/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6168 - mae: 0.7295 - val_loss: 0.7116 - val_mae: 0.4405\n",
      "Epoch 165/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6084 - mae: 0.7244 - val_loss: 0.7077 - val_mae: 0.4367\n",
      "Epoch 166/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.6002 - mae: 0.7245 - val_loss: 0.7107 - val_mae: 0.4346\n",
      "Epoch 167/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5916 - mae: 0.7159 - val_loss: 0.7131 - val_mae: 0.4326\n",
      "Epoch 168/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5929 - mae: 0.7141 - val_loss: 0.7086 - val_mae: 0.4283\n",
      "Epoch 169/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5824 - mae: 0.7112 - val_loss: 0.7080 - val_mae: 0.4247\n",
      "Epoch 170/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5704 - mae: 0.7026 - val_loss: 0.7040 - val_mae: 0.4209\n",
      "Epoch 171/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5666 - mae: 0.7036 - val_loss: 0.7069 - val_mae: 0.4218\n",
      "Epoch 172/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5567 - mae: 0.6999 - val_loss: 0.7042 - val_mae: 0.4233\n",
      "Epoch 173/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5556 - mae: 0.6966 - val_loss: 0.7058 - val_mae: 0.4268\n",
      "Epoch 174/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5446 - mae: 0.6998 - val_loss: 0.7017 - val_mae: 0.4281\n",
      "Epoch 175/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5371 - mae: 0.6968 - val_loss: 0.7027 - val_mae: 0.4310\n",
      "Epoch 176/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5333 - mae: 0.6962 - val_loss: 0.7037 - val_mae: 0.4340\n",
      "Epoch 177/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5271 - mae: 0.6981 - val_loss: 0.7031 - val_mae: 0.4360\n",
      "Epoch 178/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5177 - mae: 0.6902 - val_loss: 0.7038 - val_mae: 0.4393\n",
      "Epoch 179/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5156 - mae: 0.6866 - val_loss: 0.7077 - val_mae: 0.4431\n",
      "Epoch 180/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5074 - mae: 0.6911 - val_loss: 0.7026 - val_mae: 0.4444\n",
      "Epoch 181/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4988 - mae: 0.6861 - val_loss: 0.7036 - val_mae: 0.4470\n",
      "Epoch 182/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4983 - mae: 0.6872 - val_loss: 0.7059 - val_mae: 0.4502\n",
      "Epoch 183/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4898 - mae: 0.6855 - val_loss: 0.7046 - val_mae: 0.4526\n",
      "Epoch 184/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4827 - mae: 0.6851 - val_loss: 0.7082 - val_mae: 0.4561\n",
      "Epoch 185/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4811 - mae: 0.6757 - val_loss: 0.7130 - val_mae: 0.4603\n",
      "Epoch 186/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4717 - mae: 0.6759 - val_loss: 0.7069 - val_mae: 0.4616\n",
      "Epoch 187/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4666 - mae: 0.6799 - val_loss: 0.7113 - val_mae: 0.4650\n",
      "Epoch 188/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4600 - mae: 0.6736 - val_loss: 0.7157 - val_mae: 0.4693\n",
      "Epoch 189/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4588 - mae: 0.6742 - val_loss: 0.7129 - val_mae: 0.4706\n",
      "Epoch 190/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4510 - mae: 0.6735 - val_loss: 0.7174 - val_mae: 0.4744\n",
      "Epoch 191/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4428 - mae: 0.6705 - val_loss: 0.7222 - val_mae: 0.4781\n",
      "Epoch 192/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4458 - mae: 0.6682 - val_loss: 0.7184 - val_mae: 0.4798\n",
      "Epoch 193/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4345 - mae: 0.6673 - val_loss: 0.7221 - val_mae: 0.4832\n",
      "Epoch 194/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4325 - mae: 0.6632 - val_loss: 0.7208 - val_mae: 0.4857\n",
      "Epoch 195/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4275 - mae: 0.6640 - val_loss: 0.7251 - val_mae: 0.4893\n",
      "Epoch 196/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4168 - mae: 0.6649 - val_loss: 0.7306 - val_mae: 0.4938\n",
      "Epoch 197/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4136 - mae: 0.6573 - val_loss: 0.7342 - val_mae: 0.4970\n",
      "Epoch 198/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4127 - mae: 0.6584 - val_loss: 0.7405 - val_mae: 0.5014\n",
      "Epoch 199/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4118 - mae: 0.6572 - val_loss: 0.7435 - val_mae: 0.5041\n",
      "Epoch 200/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4010 - mae: 0.6536 - val_loss: 0.7452 - val_mae: 0.5065\n",
      "Epoch 201/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.3962 - mae: 0.6580 - val_loss: 0.7519 - val_mae: 0.5109\n",
      "Epoch 202/500\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 1.3918 - mae: 0.6531 - val_loss: 0.7560 - val_mae: 0.5145\n",
      "Epoch 203/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3923 - mae: 0.6549 - val_loss: 0.7569 - val_mae: 0.5168\n",
      "Epoch 204/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3856 - mae: 0.6514 - val_loss: 0.7610 - val_mae: 0.5205\n",
      "Epoch 205/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3783 - mae: 0.6503 - val_loss: 0.7675 - val_mae: 0.5247\n",
      "Epoch 206/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3750 - mae: 0.6532 - val_loss: 0.7745 - val_mae: 0.5294\n",
      "Epoch 207/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3698 - mae: 0.6462 - val_loss: 0.7799 - val_mae: 0.5332\n",
      "Epoch 208/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3682 - mae: 0.6437 - val_loss: 0.7887 - val_mae: 0.5383\n",
      "Epoch 209/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3638 - mae: 0.6459 - val_loss: 0.7910 - val_mae: 0.5413\n",
      "Epoch 210/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3546 - mae: 0.6436 - val_loss: 0.7940 - val_mae: 0.5443\n",
      "Epoch 211/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3543 - mae: 0.6477 - val_loss: 0.8002 - val_mae: 0.5482\n",
      "Epoch 212/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3494 - mae: 0.6451 - val_loss: 0.8063 - val_mae: 0.5521\n",
      "Epoch 213/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3457 - mae: 0.6461 - val_loss: 0.8148 - val_mae: 0.5565\n",
      "Epoch 214/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3422 - mae: 0.6439 - val_loss: 0.8244 - val_mae: 0.5613\n",
      "Epoch 215/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3348 - mae: 0.6446 - val_loss: 0.8285 - val_mae: 0.5643\n",
      "Epoch 216/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3357 - mae: 0.6444 - val_loss: 0.8265 - val_mae: 0.5660\n",
      "Epoch 217/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3281 - mae: 0.6470 - val_loss: 0.8362 - val_mae: 0.5708\n",
      "Epoch 218/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3227 - mae: 0.6416 - val_loss: 0.8479 - val_mae: 0.5758\n",
      "Epoch 219/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3237 - mae: 0.6415 - val_loss: 0.8552 - val_mae: 0.5795\n",
      "Epoch 220/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3180 - mae: 0.6404 - val_loss: 0.8616 - val_mae: 0.5830\n",
      "Epoch 221/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3137 - mae: 0.6383 - val_loss: 0.8597 - val_mae: 0.5844\n",
      "Epoch 222/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3096 - mae: 0.6392 - val_loss: 0.8576 - val_mae: 0.5860\n",
      "Epoch 223/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3063 - mae: 0.6455 - val_loss: 0.8560 - val_mae: 0.5872\n",
      "Epoch 224/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3033 - mae: 0.6366 - val_loss: 0.8542 - val_mae: 0.5886\n",
      "Epoch 225/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2964 - mae: 0.6361 - val_loss: 0.8525 - val_mae: 0.5899\n",
      "Epoch 226/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2945 - mae: 0.6392 - val_loss: 0.8504 - val_mae: 0.5916\n",
      "Epoch 227/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2938 - mae: 0.6373 - val_loss: 0.8490 - val_mae: 0.5927\n",
      "Epoch 228/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2869 - mae: 0.6365 - val_loss: 0.8475 - val_mae: 0.5939\n",
      "Epoch 229/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2851 - mae: 0.6377 - val_loss: 0.8457 - val_mae: 0.5954\n",
      "Epoch 230/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2812 - mae: 0.6338 - val_loss: 0.8439 - val_mae: 0.5968\n",
      "Epoch 231/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2760 - mae: 0.6356 - val_loss: 0.8425 - val_mae: 0.5980\n",
      "Epoch 232/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2728 - mae: 0.6344 - val_loss: 0.8410 - val_mae: 0.5992\n",
      "Epoch 233/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2704 - mae: 0.6311 - val_loss: 0.8397 - val_mae: 0.6003\n",
      "Epoch 234/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2654 - mae: 0.6333 - val_loss: 0.8381 - val_mae: 0.6016\n",
      "Epoch 235/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2633 - mae: 0.6330 - val_loss: 0.8365 - val_mae: 0.6029\n",
      "Epoch 236/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2574 - mae: 0.6305 - val_loss: 0.8351 - val_mae: 0.6041\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 237/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2597 - mae: 0.6351 - val_loss: 0.8340 - val_mae: 0.6051\n",
      "Epoch 238/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2524 - mae: 0.6287 - val_loss: 0.8325 - val_mae: 0.6064\n",
      "Epoch 239/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2485 - mae: 0.6275 - val_loss: 0.8310 - val_mae: 0.6077\n",
      "Epoch 240/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2475 - mae: 0.6292 - val_loss: 0.8298 - val_mae: 0.6088\n",
      "Epoch 241/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2405 - mae: 0.6297 - val_loss: 0.8285 - val_mae: 0.6099\n",
      "Epoch 242/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2398 - mae: 0.6272 - val_loss: 0.8273 - val_mae: 0.6110\n",
      "Epoch 243/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2358 - mae: 0.6298 - val_loss: 0.8261 - val_mae: 0.6121\n",
      "Epoch 244/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2327 - mae: 0.6262 - val_loss: 0.8253 - val_mae: 0.6128\n",
      "Epoch 245/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2319 - mae: 0.6268 - val_loss: 0.8242 - val_mae: 0.6138\n",
      "Epoch 246/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2275 - mae: 0.6281 - val_loss: 0.8231 - val_mae: 0.6148\n",
      "Epoch 247/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2235 - mae: 0.6248 - val_loss: 0.8218 - val_mae: 0.6161\n",
      "Epoch 248/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2223 - mae: 0.6218 - val_loss: 0.8202 - val_mae: 0.6175\n",
      "Epoch 249/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2168 - mae: 0.6260 - val_loss: 0.8193 - val_mae: 0.6184\n",
      "Epoch 250/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2149 - mae: 0.6209 - val_loss: 0.8183 - val_mae: 0.6193\n",
      "Epoch 251/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2128 - mae: 0.6232 - val_loss: 0.8174 - val_mae: 0.6202\n",
      "Epoch 252/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2091 - mae: 0.6189 - val_loss: 0.8166 - val_mae: 0.6210\n",
      "Epoch 253/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2087 - mae: 0.6223 - val_loss: 0.8157 - val_mae: 0.6219\n",
      "Epoch 254/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2025 - mae: 0.6176 - val_loss: 0.8147 - val_mae: 0.6228\n",
      "Epoch 255/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2002 - mae: 0.6207 - val_loss: 0.8139 - val_mae: 0.6236\n",
      "Epoch 256/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2008 - mae: 0.6173 - val_loss: 0.8130 - val_mae: 0.6245\n",
      "Epoch 257/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1949 - mae: 0.6163 - val_loss: 0.8120 - val_mae: 0.6255\n",
      "Epoch 258/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1934 - mae: 0.6151 - val_loss: 0.8112 - val_mae: 0.6263\n",
      "Epoch 259/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1900 - mae: 0.6133 - val_loss: 0.8104 - val_mae: 0.6272\n",
      "Epoch 260/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1884 - mae: 0.6150 - val_loss: 0.8097 - val_mae: 0.6278\n",
      "Epoch 261/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1868 - mae: 0.6141 - val_loss: 0.8087 - val_mae: 0.6289\n",
      "Epoch 262/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1824 - mae: 0.6119 - val_loss: 0.8078 - val_mae: 0.6298\n",
      "Epoch 263/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1802 - mae: 0.6114 - val_loss: 0.8068 - val_mae: 0.6308\n",
      "Epoch 264/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1808 - mae: 0.6100 - val_loss: 0.8061 - val_mae: 0.6316\n",
      "Epoch 265/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1767 - mae: 0.6092 - val_loss: 0.8052 - val_mae: 0.6325\n",
      "Epoch 266/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1747 - mae: 0.6097 - val_loss: 0.8043 - val_mae: 0.6335\n",
      "Epoch 267/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1715 - mae: 0.6082 - val_loss: 0.8034 - val_mae: 0.6345\n",
      "Epoch 268/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1701 - mae: 0.6080 - val_loss: 0.8025 - val_mae: 0.6354\n",
      "Epoch 269/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1670 - mae: 0.6072 - val_loss: 0.8016 - val_mae: 0.6364\n",
      "Epoch 270/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1650 - mae: 0.6081 - val_loss: 0.8009 - val_mae: 0.6372\n",
      "Epoch 271/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1631 - mae: 0.6062 - val_loss: 0.8001 - val_mae: 0.6381\n",
      "Epoch 272/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1623 - mae: 0.6085 - val_loss: 0.7994 - val_mae: 0.6388\n",
      "Epoch 273/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1579 - mae: 0.6025 - val_loss: 0.7988 - val_mae: 0.6395\n",
      "Epoch 274/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1606 - mae: 0.6060 - val_loss: 0.7980 - val_mae: 0.6404\n",
      "Epoch 275/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1545 - mae: 0.6004 - val_loss: 0.7971 - val_mae: 0.6414\n",
      "Epoch 276/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1560 - mae: 0.6024 - val_loss: 0.7965 - val_mae: 0.6422\n",
      "Epoch 277/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1516 - mae: 0.6026 - val_loss: 0.7959 - val_mae: 0.6429\n",
      "Epoch 278/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1531 - mae: 0.6010 - val_loss: 0.7955 - val_mae: 0.6434\n",
      "Epoch 279/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1479 - mae: 0.5988 - val_loss: 0.7949 - val_mae: 0.6441\n",
      "Epoch 280/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1448 - mae: 0.5973 - val_loss: 0.7941 - val_mae: 0.6450\n",
      "Epoch 281/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1437 - mae: 0.5970 - val_loss: 0.7934 - val_mae: 0.6458\n",
      "Epoch 282/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1463 - mae: 0.5996 - val_loss: 0.7929 - val_mae: 0.6465\n",
      "Epoch 283/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1422 - mae: 0.5981 - val_loss: 0.7921 - val_mae: 0.6474\n",
      "Epoch 284/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1401 - mae: 0.5992 - val_loss: 0.7915 - val_mae: 0.6482\n",
      "Epoch 285/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1364 - mae: 0.5954 - val_loss: 0.7908 - val_mae: 0.6491\n",
      "Epoch 286/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1397 - mae: 0.5971 - val_loss: 0.7903 - val_mae: 0.6497\n",
      "Epoch 287/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1346 - mae: 0.5950 - val_loss: 0.7897 - val_mae: 0.6504\n",
      "Epoch 288/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1337 - mae: 0.5943 - val_loss: 0.7891 - val_mae: 0.6512\n",
      "Epoch 289/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1331 - mae: 0.5923 - val_loss: 0.7886 - val_mae: 0.6518\n",
      "Epoch 290/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1294 - mae: 0.5922 - val_loss: 0.7880 - val_mae: 0.6526\n",
      "Epoch 291/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1274 - mae: 0.5890 - val_loss: 0.7876 - val_mae: 0.6531\n",
      "Epoch 292/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1303 - mae: 0.5949 - val_loss: 0.7870 - val_mae: 0.6538\n",
      "Epoch 293/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1247 - mae: 0.5925 - val_loss: 0.7863 - val_mae: 0.6547\n",
      "Epoch 294/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1255 - mae: 0.5903 - val_loss: 0.7858 - val_mae: 0.6553\n",
      "Epoch 295/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1238 - mae: 0.5888 - val_loss: 0.7854 - val_mae: 0.6559\n",
      "Epoch 296/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1207 - mae: 0.5876 - val_loss: 0.7849 - val_mae: 0.6566\n",
      "Epoch 297/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1208 - mae: 0.5867 - val_loss: 0.7845 - val_mae: 0.6572\n",
      "Epoch 298/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1197 - mae: 0.5882 - val_loss: 0.7839 - val_mae: 0.6580\n",
      "Epoch 299/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1176 - mae: 0.5898 - val_loss: 0.7835 - val_mae: 0.6585\n",
      "Epoch 300/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1175 - mae: 0.5875 - val_loss: 0.7830 - val_mae: 0.6592\n",
      "Epoch 301/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1151 - mae: 0.5860 - val_loss: 0.7825 - val_mae: 0.6598\n",
      "Epoch 302/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1138 - mae: 0.5862 - val_loss: 0.7821 - val_mae: 0.6604\n",
      "Epoch 303/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1145 - mae: 0.5856 - val_loss: 0.7820 - val_mae: 0.6606\n",
      "Epoch 304/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1107 - mae: 0.5839 - val_loss: 0.7816 - val_mae: 0.6612\n",
      "Epoch 305/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1105 - mae: 0.5837 - val_loss: 0.7811 - val_mae: 0.6619\n",
      "Epoch 306/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1096 - mae: 0.5839 - val_loss: 0.7806 - val_mae: 0.6625\n",
      "Epoch 307/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1092 - mae: 0.5837 - val_loss: 0.7803 - val_mae: 0.6629\n",
      "Epoch 308/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1064 - mae: 0.5796 - val_loss: 0.7800 - val_mae: 0.6634\n",
      "Epoch 309/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1066 - mae: 0.5792 - val_loss: 0.7796 - val_mae: 0.6640\n",
      "Epoch 310/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1058 - mae: 0.5804 - val_loss: 0.7791 - val_mae: 0.6647\n",
      "Epoch 311/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1043 - mae: 0.5802 - val_loss: 0.7788 - val_mae: 0.6652\n",
      "Epoch 312/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1024 - mae: 0.5799 - val_loss: 0.7786 - val_mae: 0.6655\n",
      "Epoch 313/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1022 - mae: 0.5791 - val_loss: 0.7784 - val_mae: 0.6657\n",
      "Epoch 314/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1017 - mae: 0.5799 - val_loss: 0.7779 - val_mae: 0.6665\n",
      "Epoch 315/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0999 - mae: 0.5772 - val_loss: 0.7776 - val_mae: 0.6669\n",
      "Epoch 316/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0980 - mae: 0.5759 - val_loss: 0.7773 - val_mae: 0.6675\n",
      "Epoch 317/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0987 - mae: 0.5719 - val_loss: 0.7770 - val_mae: 0.6678\n",
      "Epoch 318/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0972 - mae: 0.5784 - val_loss: 0.7767 - val_mae: 0.6684\n",
      "Epoch 319/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0958 - mae: 0.5728 - val_loss: 0.7765 - val_mae: 0.6687\n",
      "Epoch 320/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0941 - mae: 0.5714 - val_loss: 0.7761 - val_mae: 0.6693\n",
      "Epoch 321/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0939 - mae: 0.5736 - val_loss: 0.7757 - val_mae: 0.6699\n",
      "Epoch 322/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0926 - mae: 0.5735 - val_loss: 0.7754 - val_mae: 0.6703\n",
      "Epoch 323/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0919 - mae: 0.5709 - val_loss: 0.7750 - val_mae: 0.6709\n",
      "Epoch 324/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0917 - mae: 0.5700 - val_loss: 0.7751 - val_mae: 0.6709\n",
      "Epoch 325/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0909 - mae: 0.5720 - val_loss: 0.7748 - val_mae: 0.6713\n",
      "Epoch 326/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0889 - mae: 0.5684 - val_loss: 0.7744 - val_mae: 0.6719\n",
      "Epoch 327/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0882 - mae: 0.5694 - val_loss: 0.7742 - val_mae: 0.6722\n",
      "Epoch 328/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0869 - mae: 0.5661 - val_loss: 0.7739 - val_mae: 0.6726\n",
      "Epoch 329/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0866 - mae: 0.5661 - val_loss: 0.7736 - val_mae: 0.6731\n",
      "Epoch 330/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0881 - mae: 0.5717 - val_loss: 0.7734 - val_mae: 0.6735\n",
      "Epoch 331/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0852 - mae: 0.5680 - val_loss: 0.7732 - val_mae: 0.6738\n",
      "Epoch 332/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0831 - mae: 0.5661 - val_loss: 0.7727 - val_mae: 0.6747\n",
      "Epoch 333/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0830 - mae: 0.5675 - val_loss: 0.7726 - val_mae: 0.6748\n",
      "Epoch 334/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0831 - mae: 0.5666 - val_loss: 0.7724 - val_mae: 0.6752\n",
      "Epoch 335/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0818 - mae: 0.5648 - val_loss: 0.7721 - val_mae: 0.6757\n",
      "Epoch 336/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0803 - mae: 0.5656 - val_loss: 0.7718 - val_mae: 0.6763\n",
      "Epoch 337/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0803 - mae: 0.5667 - val_loss: 0.7715 - val_mae: 0.6767\n",
      "Epoch 338/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0798 - mae: 0.5615 - val_loss: 0.7714 - val_mae: 0.6768\n",
      "Epoch 339/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0784 - mae: 0.5647 - val_loss: 0.7713 - val_mae: 0.6771\n",
      "Epoch 340/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0775 - mae: 0.5625 - val_loss: 0.7709 - val_mae: 0.6778\n",
      "Epoch 341/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0774 - mae: 0.5623 - val_loss: 0.7708 - val_mae: 0.6778\n",
      "Epoch 342/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0771 - mae: 0.5634 - val_loss: 0.7708 - val_mae: 0.6778\n",
      "Epoch 343/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0754 - mae: 0.5610 - val_loss: 0.7706 - val_mae: 0.6782\n",
      "Epoch 344/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0763 - mae: 0.5634 - val_loss: 0.7704 - val_mae: 0.6785\n",
      "Epoch 345/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0744 - mae: 0.5609 - val_loss: 0.7702 - val_mae: 0.6789\n",
      "Epoch 346/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0738 - mae: 0.5597 - val_loss: 0.7701 - val_mae: 0.6792\n",
      "Epoch 347/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0734 - mae: 0.5594 - val_loss: 0.7699 - val_mae: 0.6795\n",
      "Epoch 348/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0715 - mae: 0.5570 - val_loss: 0.7697 - val_mae: 0.6798\n",
      "Epoch 349/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0708 - mae: 0.5574 - val_loss: 0.7696 - val_mae: 0.6800\n",
      "Epoch 350/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0737 - mae: 0.5637 - val_loss: 0.7694 - val_mae: 0.6804\n",
      "Epoch 351/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0708 - mae: 0.5583 - val_loss: 0.7692 - val_mae: 0.6807\n",
      "Epoch 352/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0695 - mae: 0.5580 - val_loss: 0.7690 - val_mae: 0.6810\n",
      "Epoch 353/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0688 - mae: 0.5547 - val_loss: 0.7690 - val_mae: 0.6810\n",
      "Epoch 354/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0688 - mae: 0.5547 - val_loss: 0.7688 - val_mae: 0.6814\n",
      "Epoch 355/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0678 - mae: 0.5595 - val_loss: 0.7686 - val_mae: 0.6818\n",
      "Epoch 356/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0672 - mae: 0.5582 - val_loss: 0.7684 - val_mae: 0.6822\n",
      "Epoch 357/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0662 - mae: 0.5536 - val_loss: 0.7683 - val_mae: 0.6824\n",
      "Epoch 358/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0666 - mae: 0.5557 - val_loss: 0.7682 - val_mae: 0.6825\n",
      "Epoch 359/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0654 - mae: 0.5531 - val_loss: 0.7680 - val_mae: 0.6829\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 360/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0648 - mae: 0.5556 - val_loss: 0.7679 - val_mae: 0.6831\n",
      "Epoch 361/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0643 - mae: 0.5562 - val_loss: 0.7677 - val_mae: 0.6834\n",
      "Epoch 362/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0631 - mae: 0.5523 - val_loss: 0.7676 - val_mae: 0.6837\n",
      "Epoch 363/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0626 - mae: 0.5524 - val_loss: 0.7675 - val_mae: 0.6839\n",
      "Epoch 364/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0621 - mae: 0.5538 - val_loss: 0.7672 - val_mae: 0.6844\n",
      "Epoch 365/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0628 - mae: 0.5529 - val_loss: 0.7671 - val_mae: 0.6846\n",
      "Epoch 366/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0610 - mae: 0.5525 - val_loss: 0.7669 - val_mae: 0.6850\n",
      "Epoch 367/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0614 - mae: 0.5526 - val_loss: 0.7668 - val_mae: 0.6852\n",
      "Epoch 368/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0599 - mae: 0.5507 - val_loss: 0.7666 - val_mae: 0.6855\n",
      "Epoch 369/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0590 - mae: 0.5481 - val_loss: 0.7666 - val_mae: 0.6857\n",
      "Epoch 370/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0587 - mae: 0.5495 - val_loss: 0.7663 - val_mae: 0.6861\n",
      "Epoch 371/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0590 - mae: 0.5523 - val_loss: 0.7661 - val_mae: 0.6866\n",
      "Epoch 372/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0569 - mae: 0.5495 - val_loss: 0.7659 - val_mae: 0.6869\n",
      "Epoch 373/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0565 - mae: 0.5488 - val_loss: 0.7657 - val_mae: 0.6873\n",
      "Epoch 374/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0559 - mae: 0.5481 - val_loss: 0.7656 - val_mae: 0.6875\n",
      "Epoch 375/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0563 - mae: 0.5455 - val_loss: 0.7656 - val_mae: 0.6875\n",
      "Epoch 376/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0551 - mae: 0.5464 - val_loss: 0.7654 - val_mae: 0.6879\n",
      "Epoch 377/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0546 - mae: 0.5446 - val_loss: 0.7653 - val_mae: 0.6881\n",
      "Epoch 378/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0574 - mae: 0.5496 - val_loss: 0.7653 - val_mae: 0.6882\n",
      "Epoch 379/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0533 - mae: 0.5434 - val_loss: 0.7652 - val_mae: 0.6883\n",
      "Epoch 380/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0533 - mae: 0.5448 - val_loss: 0.7650 - val_mae: 0.6887\n",
      "Epoch 381/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0525 - mae: 0.5438 - val_loss: 0.7650 - val_mae: 0.6888\n",
      "Epoch 382/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0525 - mae: 0.5439 - val_loss: 0.7650 - val_mae: 0.6888\n",
      "Epoch 383/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0527 - mae: 0.5419 - val_loss: 0.7649 - val_mae: 0.6891\n",
      "Epoch 384/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0514 - mae: 0.5470 - val_loss: 0.7647 - val_mae: 0.6894\n",
      "Epoch 385/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0508 - mae: 0.5434 - val_loss: 0.7647 - val_mae: 0.6893\n",
      "Epoch 386/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0500 - mae: 0.5405 - val_loss: 0.7646 - val_mae: 0.6896\n",
      "Epoch 387/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0498 - mae: 0.5403 - val_loss: 0.7645 - val_mae: 0.6898\n",
      "Epoch 388/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0490 - mae: 0.5400 - val_loss: 0.7643 - val_mae: 0.6902\n",
      "Epoch 389/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0505 - mae: 0.5422 - val_loss: 0.7642 - val_mae: 0.6904\n",
      "Epoch 390/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0483 - mae: 0.5421 - val_loss: 0.7641 - val_mae: 0.6907\n",
      "Epoch 391/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0474 - mae: 0.5408 - val_loss: 0.7639 - val_mae: 0.6911\n",
      "Epoch 392/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0474 - mae: 0.5412 - val_loss: 0.7637 - val_mae: 0.6914\n",
      "Epoch 393/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0466 - mae: 0.5376 - val_loss: 0.7636 - val_mae: 0.6917\n",
      "Epoch 394/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0464 - mae: 0.5371 - val_loss: 0.7637 - val_mae: 0.6916\n",
      "Epoch 395/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0480 - mae: 0.5429 - val_loss: 0.7637 - val_mae: 0.6915\n",
      "Epoch 396/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0456 - mae: 0.5398 - val_loss: 0.7637 - val_mae: 0.6915\n",
      "Epoch 397/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0454 - mae: 0.5365 - val_loss: 0.7636 - val_mae: 0.6917\n",
      "Epoch 398/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0446 - mae: 0.5345 - val_loss: 0.7634 - val_mae: 0.6920\n",
      "Epoch 399/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0446 - mae: 0.5368 - val_loss: 0.7634 - val_mae: 0.6922\n",
      "Epoch 400/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0437 - mae: 0.5332 - val_loss: 0.7632 - val_mae: 0.6926\n",
      "Epoch 401/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0448 - mae: 0.5343 - val_loss: 0.7631 - val_mae: 0.6928\n",
      "Epoch 402/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0430 - mae: 0.5370 - val_loss: 0.7629 - val_mae: 0.6931\n",
      "Epoch 403/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0426 - mae: 0.5350 - val_loss: 0.7628 - val_mae: 0.6934\n",
      "Epoch 404/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0427 - mae: 0.5323 - val_loss: 0.7627 - val_mae: 0.6937\n",
      "Epoch 405/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0419 - mae: 0.5317 - val_loss: 0.7626 - val_mae: 0.6939\n",
      "Epoch 406/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0417 - mae: 0.5333 - val_loss: 0.7624 - val_mae: 0.6943\n",
      "Epoch 407/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0409 - mae: 0.5305 - val_loss: 0.7623 - val_mae: 0.6946\n",
      "Epoch 408/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0407 - mae: 0.5307 - val_loss: 0.7622 - val_mae: 0.6948\n",
      "Epoch 409/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0402 - mae: 0.5312 - val_loss: 0.7621 - val_mae: 0.6950\n",
      "Epoch 410/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0401 - mae: 0.5276 - val_loss: 0.7620 - val_mae: 0.6953\n",
      "Epoch 411/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0401 - mae: 0.5297 - val_loss: 0.7619 - val_mae: 0.6955\n",
      "Epoch 412/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0395 - mae: 0.5255 - val_loss: 0.7619 - val_mae: 0.6954\n",
      "Epoch 413/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0400 - mae: 0.5277 - val_loss: 0.7618 - val_mae: 0.6956\n",
      "Epoch 414/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0386 - mae: 0.5265 - val_loss: 0.7618 - val_mae: 0.6957\n",
      "Epoch 415/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0381 - mae: 0.5274 - val_loss: 0.7617 - val_mae: 0.6958\n",
      "Epoch 416/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0384 - mae: 0.5242 - val_loss: 0.7618 - val_mae: 0.6958\n",
      "Epoch 417/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0382 - mae: 0.5239 - val_loss: 0.7618 - val_mae: 0.6957\n",
      "Epoch 418/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0379 - mae: 0.5242 - val_loss: 0.7617 - val_mae: 0.6959\n",
      "Epoch 419/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0379 - mae: 0.5236 - val_loss: 0.7617 - val_mae: 0.6960\n",
      "Epoch 420/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0375 - mae: 0.5253 - val_loss: 0.7616 - val_mae: 0.6961\n",
      "Epoch 421/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0367 - mae: 0.5225 - val_loss: 0.7615 - val_mae: 0.6964\n",
      "Epoch 422/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0368 - mae: 0.5221 - val_loss: 0.7615 - val_mae: 0.6965\n",
      "Epoch 423/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0367 - mae: 0.5199 - val_loss: 0.7615 - val_mae: 0.6963\n",
      "Epoch 424/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0363 - mae: 0.5189 - val_loss: 0.7614 - val_mae: 0.6966\n",
      "Epoch 425/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0356 - mae: 0.5174 - val_loss: 0.7613 - val_mae: 0.6968\n",
      "Epoch 426/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0358 - mae: 0.5161 - val_loss: 0.7614 - val_mae: 0.6967\n",
      "Epoch 427/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0355 - mae: 0.5174 - val_loss: 0.7613 - val_mae: 0.6969\n",
      "Epoch 428/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0350 - mae: 0.5166 - val_loss: 0.7612 - val_mae: 0.6971\n",
      "Epoch 429/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0349 - mae: 0.5173 - val_loss: 0.7611 - val_mae: 0.6973\n",
      "Epoch 430/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0349 - mae: 0.5144 - val_loss: 0.7611 - val_mae: 0.6973\n",
      "Epoch 431/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0342 - mae: 0.5122 - val_loss: 0.7611 - val_mae: 0.6974\n",
      "Epoch 432/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0343 - mae: 0.5140 - val_loss: 0.7610 - val_mae: 0.6975\n",
      "Epoch 433/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0343 - mae: 0.5125 - val_loss: 0.7610 - val_mae: 0.6976\n",
      "Epoch 434/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0346 - mae: 0.5119 - val_loss: 0.7611 - val_mae: 0.6972\n",
      "Epoch 435/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0340 - mae: 0.5110 - val_loss: 0.7611 - val_mae: 0.6974\n",
      "Epoch 436/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0337 - mae: 0.5089 - val_loss: 0.7609 - val_mae: 0.6977\n",
      "Epoch 437/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0331 - mae: 0.5072 - val_loss: 0.7609 - val_mae: 0.6977\n",
      "Epoch 438/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0340 - mae: 0.5049 - val_loss: 0.7610 - val_mae: 0.6975\n",
      "Epoch 439/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0335 - mae: 0.5077 - val_loss: 0.7609 - val_mae: 0.6978\n",
      "Epoch 440/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0333 - mae: 0.5049 - val_loss: 0.7608 - val_mae: 0.6981\n",
      "Epoch 441/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0335 - mae: 0.5072 - val_loss: 0.7608 - val_mae: 0.6982\n",
      "Epoch 442/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0332 - mae: 0.5082 - val_loss: 0.7606 - val_mae: 0.6984\n",
      "Epoch 443/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0332 - mae: 0.5049 - val_loss: 0.7606 - val_mae: 0.6985\n",
      "Epoch 444/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0333 - mae: 0.5058 - val_loss: 0.7606 - val_mae: 0.6985\n",
      "Epoch 445/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0333 - mae: 0.5086 - val_loss: 0.7605 - val_mae: 0.6987\n",
      "Epoch 446/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0329 - mae: 0.5091 - val_loss: 0.7604 - val_mae: 0.6989\n",
      "Epoch 447/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0328 - mae: 0.5069 - val_loss: 0.7603 - val_mae: 0.6992\n",
      "Epoch 448/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0329 - mae: 0.5063 - val_loss: 0.7603 - val_mae: 0.6994\n",
      "Epoch 449/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0326 - mae: 0.5048 - val_loss: 0.7602 - val_mae: 0.6996\n",
      "Epoch 450/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0324 - mae: 0.5091 - val_loss: 0.7601 - val_mae: 0.6997\n",
      "Epoch 451/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0324 - mae: 0.5078 - val_loss: 0.7600 - val_mae: 0.6999\n",
      "Epoch 452/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0324 - mae: 0.5083 - val_loss: 0.7600 - val_mae: 0.7000\n",
      "Epoch 453/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0331 - mae: 0.5101 - val_loss: 0.7601 - val_mae: 0.6998\n",
      "Epoch 454/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0324 - mae: 0.5039 - val_loss: 0.7600 - val_mae: 0.7001\n",
      "Epoch 455/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0325 - mae: 0.5077 - val_loss: 0.7599 - val_mae: 0.7003\n",
      "Epoch 456/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0321 - mae: 0.5092 - val_loss: 0.7598 - val_mae: 0.7005\n",
      "Epoch 457/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0326 - mae: 0.5082 - val_loss: 0.7598 - val_mae: 0.7006\n",
      "Epoch 458/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0320 - mae: 0.5082 - val_loss: 0.7597 - val_mae: 0.7008\n",
      "Epoch 459/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0319 - mae: 0.5079 - val_loss: 0.7596 - val_mae: 0.7010\n",
      "Epoch 460/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0320 - mae: 0.5031 - val_loss: 0.7596 - val_mae: 0.7010\n",
      "Epoch 461/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0318 - mae: 0.5099 - val_loss: 0.7595 - val_mae: 0.7012\n",
      "Epoch 462/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0319 - mae: 0.5101 - val_loss: 0.7595 - val_mae: 0.7013\n",
      "Epoch 463/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0325 - mae: 0.5085 - val_loss: 0.7596 - val_mae: 0.7011\n",
      "Epoch 464/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0318 - mae: 0.5082 - val_loss: 0.7596 - val_mae: 0.7011\n",
      "Epoch 465/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0324 - mae: 0.5069 - val_loss: 0.7597 - val_mae: 0.7009\n",
      "Epoch 466/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0322 - mae: 0.5107 - val_loss: 0.7595 - val_mae: 0.7012\n",
      "Epoch 467/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0318 - mae: 0.5066 - val_loss: 0.7594 - val_mae: 0.7015\n",
      "Epoch 468/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0319 - mae: 0.5084 - val_loss: 0.7594 - val_mae: 0.7016\n",
      "Epoch 469/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0318 - mae: 0.5099 - val_loss: 0.7593 - val_mae: 0.7018\n",
      "Epoch 470/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0316 - mae: 0.5089 - val_loss: 0.7592 - val_mae: 0.7020\n",
      "Epoch 471/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0314 - mae: 0.5056 - val_loss: 0.7591 - val_mae: 0.7022\n",
      "Epoch 472/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0321 - mae: 0.5074 - val_loss: 0.7592 - val_mae: 0.7020\n",
      "Epoch 473/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0319 - mae: 0.5102 - val_loss: 0.7592 - val_mae: 0.7022\n",
      "Epoch 474/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0313 - mae: 0.5100 - val_loss: 0.7591 - val_mae: 0.7022\n",
      "Epoch 475/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0315 - mae: 0.5082 - val_loss: 0.7591 - val_mae: 0.7024\n",
      "Epoch 476/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0321 - mae: 0.5078 - val_loss: 0.7593 - val_mae: 0.7019\n",
      "Epoch 477/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0317 - mae: 0.5122 - val_loss: 0.7592 - val_mae: 0.7021\n",
      "Epoch 478/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0314 - mae: 0.5080 - val_loss: 0.7591 - val_mae: 0.7024\n",
      "Epoch 479/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0318 - mae: 0.5086 - val_loss: 0.7591 - val_mae: 0.7024\n",
      "Epoch 480/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0315 - mae: 0.5094 - val_loss: 0.7590 - val_mae: 0.7027\n",
      "Epoch 481/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0314 - mae: 0.5114 - val_loss: 0.7589 - val_mae: 0.7028\n",
      "Epoch 482/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0312 - mae: 0.5085 - val_loss: 0.7588 - val_mae: 0.7030\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 483/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0313 - mae: 0.5095 - val_loss: 0.7588 - val_mae: 0.7032\n",
      "Epoch 484/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0315 - mae: 0.5079 - val_loss: 0.7588 - val_mae: 0.7031\n",
      "Epoch 485/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0316 - mae: 0.5112 - val_loss: 0.7588 - val_mae: 0.7031\n",
      "Epoch 486/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0316 - mae: 0.5094 - val_loss: 0.7588 - val_mae: 0.7031\n",
      "Epoch 487/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0312 - mae: 0.5091 - val_loss: 0.7587 - val_mae: 0.7033\n",
      "Epoch 488/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0310 - mae: 0.5107 - val_loss: 0.7587 - val_mae: 0.7035\n",
      "Epoch 489/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0311 - mae: 0.5111 - val_loss: 0.7586 - val_mae: 0.7037\n",
      "Epoch 490/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0307 - mae: 0.5093 - val_loss: 0.7586 - val_mae: 0.7037\n",
      "Epoch 491/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0310 - mae: 0.5092 - val_loss: 0.7585 - val_mae: 0.7039\n",
      "Epoch 492/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0308 - mae: 0.5139 - val_loss: 0.7584 - val_mae: 0.7041\n",
      "Epoch 493/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0308 - mae: 0.5095 - val_loss: 0.7584 - val_mae: 0.7043\n",
      "Epoch 494/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0306 - mae: 0.5100 - val_loss: 0.7583 - val_mae: 0.7045\n",
      "Epoch 495/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0314 - mae: 0.5120 - val_loss: 0.7584 - val_mae: 0.7042\n",
      "Epoch 496/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0308 - mae: 0.5103 - val_loss: 0.7583 - val_mae: 0.7044\n",
      "Epoch 497/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0309 - mae: 0.5102 - val_loss: 0.7583 - val_mae: 0.7045\n",
      "Epoch 498/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0303 - mae: 0.5122 - val_loss: 0.7582 - val_mae: 0.7046\n",
      "Epoch 499/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0312 - mae: 0.5114 - val_loss: 0.7583 - val_mae: 0.7044\n",
      "Epoch 500/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0305 - mae: 0.5097 - val_loss: 0.7583 - val_mae: 0.7045\n",
      "processing fold # 2\n",
      "Epoch 1/500\n",
      "14/14 [==============================] - 0s 7ms/step - loss: 5.5824 - mae: 1.8232 - val_loss: 7.3193 - val_mae: 2.0794\n",
      "Epoch 2/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 5.4275 - mae: 1.7840 - val_loss: 7.2307 - val_mae: 2.0481\n",
      "Epoch 3/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 5.2943 - mae: 1.7490 - val_loss: 7.1517 - val_mae: 2.0199\n",
      "Epoch 4/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 5.1687 - mae: 1.7136 - val_loss: 7.0721 - val_mae: 1.9899\n",
      "Epoch 5/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 5.0443 - mae: 1.6772 - val_loss: 6.9990 - val_mae: 1.9619\n",
      "Epoch 6/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.9234 - mae: 1.6441 - val_loss: 6.9232 - val_mae: 1.9322\n",
      "Epoch 7/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.7945 - mae: 1.6096 - val_loss: 6.8461 - val_mae: 1.9007\n",
      "Epoch 8/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.6792 - mae: 1.5687 - val_loss: 6.7816 - val_mae: 1.8731\n",
      "Epoch 9/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.5626 - mae: 1.5365 - val_loss: 6.7107 - val_mae: 1.8422\n",
      "Epoch 10/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.4412 - mae: 1.5000 - val_loss: 6.6414 - val_mae: 1.8106\n",
      "Epoch 11/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.3187 - mae: 1.4651 - val_loss: 6.5723 - val_mae: 1.7780\n",
      "Epoch 12/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.2033 - mae: 1.4247 - val_loss: 6.5079 - val_mae: 1.7459\n",
      "Epoch 13/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 4.0957 - mae: 1.3857 - val_loss: 6.4542 - val_mae: 1.7190\n",
      "Epoch 14/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.9862 - mae: 1.3535 - val_loss: 6.3916 - val_mae: 1.6852\n",
      "Epoch 15/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.8747 - mae: 1.3178 - val_loss: 6.3353 - val_mae: 1.6545\n",
      "Epoch 16/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.7726 - mae: 1.2790 - val_loss: 6.2801 - val_mae: 1.6253\n",
      "Epoch 17/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.6791 - mae: 1.2401 - val_loss: 6.2126 - val_mae: 1.5946\n",
      "Epoch 18/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.5833 - mae: 1.2062 - val_loss: 6.1371 - val_mae: 1.5602\n",
      "Epoch 19/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.4925 - mae: 1.1792 - val_loss: 6.0732 - val_mae: 1.5313\n",
      "Epoch 20/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.4073 - mae: 1.1667 - val_loss: 6.0084 - val_mae: 1.5118\n",
      "Epoch 21/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.3166 - mae: 1.1584 - val_loss: 5.9449 - val_mae: 1.4994\n",
      "Epoch 22/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.2310 - mae: 1.1461 - val_loss: 5.8884 - val_mae: 1.4877\n",
      "Epoch 23/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.1524 - mae: 1.1407 - val_loss: 5.8318 - val_mae: 1.4762\n",
      "Epoch 24/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 3.0752 - mae: 1.1375 - val_loss: 5.7772 - val_mae: 1.4819\n",
      "Epoch 25/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.9980 - mae: 1.1340 - val_loss: 5.7284 - val_mae: 1.4878\n",
      "Epoch 26/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.9264 - mae: 1.1303 - val_loss: 5.6808 - val_mae: 1.4938\n",
      "Epoch 27/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.8574 - mae: 1.1225 - val_loss: 5.6377 - val_mae: 1.4996\n",
      "Epoch 28/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7917 - mae: 1.1196 - val_loss: 5.5960 - val_mae: 1.5048\n",
      "Epoch 29/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7276 - mae: 1.1153 - val_loss: 5.5569 - val_mae: 1.5109\n",
      "Epoch 30/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6682 - mae: 1.1157 - val_loss: 5.5222 - val_mae: 1.5163\n",
      "Epoch 31/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6111 - mae: 1.1088 - val_loss: 5.4871 - val_mae: 1.5216\n",
      "Epoch 32/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5591 - mae: 1.1007 - val_loss: 5.4556 - val_mae: 1.5268\n",
      "Epoch 33/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5069 - mae: 1.1096 - val_loss: 5.4314 - val_mae: 1.5321\n",
      "Epoch 34/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4606 - mae: 1.1039 - val_loss: 5.4038 - val_mae: 1.5374\n",
      "Epoch 35/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.4120 - mae: 1.1028 - val_loss: 5.3786 - val_mae: 1.5429\n",
      "Epoch 36/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.3654 - mae: 1.1014 - val_loss: 5.3566 - val_mae: 1.5476\n",
      "Epoch 37/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.3264 - mae: 1.0948 - val_loss: 5.3360 - val_mae: 1.5531\n",
      "Epoch 38/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.2844 - mae: 1.0962 - val_loss: 5.3179 - val_mae: 1.5576\n",
      "Epoch 39/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.2465 - mae: 1.0933 - val_loss: 5.3013 - val_mae: 1.5626\n",
      "Epoch 40/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.2097 - mae: 1.0905 - val_loss: 5.2855 - val_mae: 1.5677\n",
      "Epoch 41/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1727 - mae: 1.0907 - val_loss: 5.2759 - val_mae: 1.5726\n",
      "Epoch 42/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1410 - mae: 1.0865 - val_loss: 5.2644 - val_mae: 1.5775\n",
      "Epoch 43/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1041 - mae: 1.0818 - val_loss: 5.2552 - val_mae: 1.5822\n",
      "Epoch 44/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0701 - mae: 1.0795 - val_loss: 5.2477 - val_mae: 1.5872\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0385 - mae: 1.0781 - val_loss: 5.2405 - val_mae: 1.5927\n",
      "Epoch 46/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0078 - mae: 1.0780 - val_loss: 5.2346 - val_mae: 1.5976\n",
      "Epoch 47/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9797 - mae: 1.0731 - val_loss: 5.2293 - val_mae: 1.6022\n",
      "Epoch 48/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9501 - mae: 1.0785 - val_loss: 5.2266 - val_mae: 1.6077\n",
      "Epoch 49/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9241 - mae: 1.0735 - val_loss: 5.2209 - val_mae: 1.6119\n",
      "Epoch 50/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9037 - mae: 1.0712 - val_loss: 5.2154 - val_mae: 1.6159\n",
      "Epoch 51/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.8743 - mae: 1.0690 - val_loss: 5.2126 - val_mae: 1.6205\n",
      "Epoch 52/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.8525 - mae: 1.0653 - val_loss: 5.2100 - val_mae: 1.6251\n",
      "Epoch 53/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.8320 - mae: 1.0636 - val_loss: 5.2072 - val_mae: 1.6290\n",
      "Epoch 54/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.8132 - mae: 1.0639 - val_loss: 5.2042 - val_mae: 1.6320\n",
      "Epoch 55/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7940 - mae: 1.0576 - val_loss: 5.1924 - val_mae: 1.6343\n",
      "Epoch 56/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7738 - mae: 1.0584 - val_loss: 5.1732 - val_mae: 1.6355\n",
      "Epoch 57/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7574 - mae: 1.0565 - val_loss: 5.1545 - val_mae: 1.6367\n",
      "Epoch 58/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7420 - mae: 1.0530 - val_loss: 5.1381 - val_mae: 1.6382\n",
      "Epoch 59/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7301 - mae: 1.0503 - val_loss: 5.1194 - val_mae: 1.6391\n",
      "Epoch 60/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7171 - mae: 1.0441 - val_loss: 5.0996 - val_mae: 1.6404\n",
      "Epoch 61/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.7020 - mae: 1.0380 - val_loss: 5.0778 - val_mae: 1.6401\n",
      "Epoch 62/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6898 - mae: 1.0362 - val_loss: 5.0588 - val_mae: 1.6407\n",
      "Epoch 63/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6769 - mae: 1.0305 - val_loss: 5.0424 - val_mae: 1.6422\n",
      "Epoch 64/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6656 - mae: 1.0287 - val_loss: 5.0231 - val_mae: 1.6422\n",
      "Epoch 65/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6555 - mae: 1.0243 - val_loss: 5.0045 - val_mae: 1.6429\n",
      "Epoch 66/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6446 - mae: 1.0207 - val_loss: 4.9861 - val_mae: 1.6429\n",
      "Epoch 67/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6396 - mae: 1.0154 - val_loss: 4.9662 - val_mae: 1.6422\n",
      "Epoch 68/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6275 - mae: 1.0102 - val_loss: 4.9478 - val_mae: 1.6424\n",
      "Epoch 69/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6186 - mae: 1.0042 - val_loss: 4.9321 - val_mae: 1.6423\n",
      "Epoch 70/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6136 - mae: 1.0011 - val_loss: 4.9166 - val_mae: 1.6424\n",
      "Epoch 71/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.6080 - mae: 0.9948 - val_loss: 4.9025 - val_mae: 1.6421\n",
      "Epoch 72/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.6007 - mae: 0.9902 - val_loss: 4.8874 - val_mae: 1.6418\n",
      "Epoch 73/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.5959 - mae: 0.9902 - val_loss: 4.8757 - val_mae: 1.6420\n",
      "Epoch 74/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5896 - mae: 0.9908 - val_loss: 4.8636 - val_mae: 1.6416\n",
      "Epoch 75/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5895 - mae: 0.9825 - val_loss: 4.8523 - val_mae: 1.6411\n",
      "Epoch 76/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5817 - mae: 0.9849 - val_loss: 4.8434 - val_mae: 1.6405\n",
      "Epoch 77/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5805 - mae: 0.9765 - val_loss: 4.8303 - val_mae: 1.6394\n",
      "Epoch 78/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5763 - mae: 0.9820 - val_loss: 4.8175 - val_mae: 1.6382\n",
      "Epoch 79/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5704 - mae: 0.9838 - val_loss: 4.8114 - val_mae: 1.6383\n",
      "Epoch 80/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5688 - mae: 0.9837 - val_loss: 4.8066 - val_mae: 1.6383\n",
      "Epoch 81/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5673 - mae: 0.9798 - val_loss: 4.8013 - val_mae: 1.6386\n",
      "Epoch 82/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5648 - mae: 0.9785 - val_loss: 4.7911 - val_mae: 1.6372\n",
      "Epoch 83/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5593 - mae: 0.9752 - val_loss: 4.7852 - val_mae: 1.6372\n",
      "Epoch 84/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5549 - mae: 0.9827 - val_loss: 4.7841 - val_mae: 1.6375\n",
      "Epoch 85/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5546 - mae: 0.9810 - val_loss: 4.7819 - val_mae: 1.6376\n",
      "Epoch 86/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5526 - mae: 0.9808 - val_loss: 4.7793 - val_mae: 1.6374\n",
      "Epoch 87/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5484 - mae: 0.9763 - val_loss: 4.7775 - val_mae: 1.6376\n",
      "Epoch 88/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5471 - mae: 0.9760 - val_loss: 4.7783 - val_mae: 1.6383\n",
      "Epoch 89/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5498 - mae: 0.9724 - val_loss: 4.7754 - val_mae: 1.6380\n",
      "Epoch 90/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5433 - mae: 0.9729 - val_loss: 4.7724 - val_mae: 1.6374\n",
      "Epoch 91/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5412 - mae: 0.9687 - val_loss: 4.7710 - val_mae: 1.6374\n",
      "Epoch 92/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5353 - mae: 0.9755 - val_loss: 4.7717 - val_mae: 1.6377\n",
      "Epoch 93/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5320 - mae: 0.9756 - val_loss: 4.7720 - val_mae: 1.6374\n",
      "Epoch 94/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.5362 - mae: 0.9701 - val_loss: 4.7721 - val_mae: 1.6372\n",
      "Epoch 95/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5317 - mae: 0.9680 - val_loss: 4.7705 - val_mae: 1.6374\n",
      "Epoch 96/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5266 - mae: 0.9687 - val_loss: 4.7721 - val_mae: 1.6382\n",
      "Epoch 97/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5270 - mae: 0.9675 - val_loss: 4.7753 - val_mae: 1.6390\n",
      "Epoch 98/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5235 - mae: 0.9687 - val_loss: 4.7774 - val_mae: 1.6391\n",
      "Epoch 99/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5229 - mae: 0.9647 - val_loss: 4.7752 - val_mae: 1.6382\n",
      "Epoch 100/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5213 - mae: 0.9629 - val_loss: 4.7741 - val_mae: 1.6383\n",
      "Epoch 101/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5170 - mae: 0.9615 - val_loss: 4.7753 - val_mae: 1.6391\n",
      "Epoch 102/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5126 - mae: 0.9661 - val_loss: 4.7783 - val_mae: 1.6396\n",
      "Epoch 103/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5186 - mae: 0.9581 - val_loss: 4.7759 - val_mae: 1.6391\n",
      "Epoch 104/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5139 - mae: 0.9538 - val_loss: 4.7762 - val_mae: 1.6390\n",
      "Epoch 105/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5126 - mae: 0.9543 - val_loss: 4.7753 - val_mae: 1.6388\n",
      "Epoch 106/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5059 - mae: 0.9550 - val_loss: 4.7774 - val_mae: 1.6394\n",
      "Epoch 107/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5091 - mae: 0.9481 - val_loss: 4.7755 - val_mae: 1.6387\n",
      "Epoch 108/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5037 - mae: 0.9517 - val_loss: 4.7764 - val_mae: 1.6387\n",
      "Epoch 109/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.5017 - mae: 0.9503 - val_loss: 4.7784 - val_mae: 1.6389\n",
      "Epoch 110/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4995 - mae: 0.9509 - val_loss: 4.7783 - val_mae: 1.6392\n",
      "Epoch 111/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4978 - mae: 0.9431 - val_loss: 4.7786 - val_mae: 1.6394\n",
      "Epoch 112/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4931 - mae: 0.9511 - val_loss: 4.7808 - val_mae: 1.6399\n",
      "Epoch 113/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4932 - mae: 0.9460 - val_loss: 4.7799 - val_mae: 1.6398\n",
      "Epoch 114/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4914 - mae: 0.9441 - val_loss: 4.7829 - val_mae: 1.6407\n",
      "Epoch 115/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4849 - mae: 0.9480 - val_loss: 4.7854 - val_mae: 1.6403\n",
      "Epoch 116/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4916 - mae: 0.9340 - val_loss: 4.7881 - val_mae: 1.6407\n",
      "Epoch 117/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4886 - mae: 0.9323 - val_loss: 4.7885 - val_mae: 1.6412\n",
      "Epoch 118/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4833 - mae: 0.9352 - val_loss: 4.7899 - val_mae: 1.6413\n",
      "Epoch 119/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4775 - mae: 0.9420 - val_loss: 4.7944 - val_mae: 1.6414\n",
      "Epoch 120/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4795 - mae: 0.9330 - val_loss: 4.7940 - val_mae: 1.6421\n",
      "Epoch 121/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4788 - mae: 0.9328 - val_loss: 4.7979 - val_mae: 1.6433\n",
      "Epoch 122/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4754 - mae: 0.9314 - val_loss: 4.7984 - val_mae: 1.6426\n",
      "Epoch 123/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4733 - mae: 0.9313 - val_loss: 4.8011 - val_mae: 1.6426\n",
      "Epoch 124/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4719 - mae: 0.9297 - val_loss: 4.8018 - val_mae: 1.6427\n",
      "Epoch 125/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4667 - mae: 0.9310 - val_loss: 4.8052 - val_mae: 1.6432\n",
      "Epoch 126/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4714 - mae: 0.9210 - val_loss: 4.8049 - val_mae: 1.6432\n",
      "Epoch 127/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4647 - mae: 0.9226 - val_loss: 4.8049 - val_mae: 1.6433\n",
      "Epoch 128/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4636 - mae: 0.9263 - val_loss: 4.8047 - val_mae: 1.6433\n",
      "Epoch 129/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4625 - mae: 0.9214 - val_loss: 4.8029 - val_mae: 1.6426\n",
      "Epoch 130/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4615 - mae: 0.9191 - val_loss: 4.8027 - val_mae: 1.6426\n",
      "Epoch 131/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4603 - mae: 0.9185 - val_loss: 4.8043 - val_mae: 1.6431\n",
      "Epoch 132/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4553 - mae: 0.9230 - val_loss: 4.8065 - val_mae: 1.6440\n",
      "Epoch 133/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4518 - mae: 0.9196 - val_loss: 4.8075 - val_mae: 1.6447\n",
      "Epoch 134/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4546 - mae: 0.9123 - val_loss: 4.8104 - val_mae: 1.6455\n",
      "Epoch 135/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.4550 - mae: 0.9135 - val_loss: 4.8131 - val_mae: 1.6465\n",
      "Epoch 136/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4480 - mae: 0.9154 - val_loss: 4.8151 - val_mae: 1.6472\n",
      "Epoch 137/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4482 - mae: 0.9118 - val_loss: 4.8196 - val_mae: 1.6480\n",
      "Epoch 138/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4438 - mae: 0.9144 - val_loss: 4.8253 - val_mae: 1.6490\n",
      "Epoch 139/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4391 - mae: 0.9170 - val_loss: 4.8312 - val_mae: 1.6487\n",
      "Epoch 140/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4425 - mae: 0.9117 - val_loss: 4.8321 - val_mae: 1.6487\n",
      "Epoch 141/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4384 - mae: 0.9108 - val_loss: 4.8350 - val_mae: 1.6486\n",
      "Epoch 142/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4381 - mae: 0.9070 - val_loss: 4.8352 - val_mae: 1.6489\n",
      "Epoch 143/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4378 - mae: 0.9035 - val_loss: 4.8355 - val_mae: 1.6492\n",
      "Epoch 144/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4334 - mae: 0.9065 - val_loss: 4.8378 - val_mae: 1.6495\n",
      "Epoch 145/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4316 - mae: 0.9039 - val_loss: 4.8382 - val_mae: 1.6496\n",
      "Epoch 146/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4309 - mae: 0.9018 - val_loss: 4.8388 - val_mae: 1.6502\n",
      "Epoch 147/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4330 - mae: 0.8975 - val_loss: 4.8418 - val_mae: 1.6514\n",
      "Epoch 148/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4258 - mae: 0.9027 - val_loss: 4.8474 - val_mae: 1.6522\n",
      "Epoch 149/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4274 - mae: 0.8972 - val_loss: 4.8493 - val_mae: 1.6531\n",
      "Epoch 150/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4249 - mae: 0.8949 - val_loss: 4.8527 - val_mae: 1.6540\n",
      "Epoch 151/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4213 - mae: 0.8975 - val_loss: 4.8548 - val_mae: 1.6546\n",
      "Epoch 152/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4180 - mae: 0.8976 - val_loss: 4.8554 - val_mae: 1.6544\n",
      "Epoch 153/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4179 - mae: 0.8948 - val_loss: 4.8559 - val_mae: 1.6542\n",
      "Epoch 154/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4145 - mae: 0.8961 - val_loss: 4.8571 - val_mae: 1.6543\n",
      "Epoch 155/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4170 - mae: 0.8862 - val_loss: 4.8591 - val_mae: 1.6547\n",
      "Epoch 156/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4112 - mae: 0.8895 - val_loss: 4.8615 - val_mae: 1.6545\n",
      "Epoch 157/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4114 - mae: 0.8884 - val_loss: 4.8636 - val_mae: 1.6552\n",
      "Epoch 158/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4077 - mae: 0.8902 - val_loss: 4.8658 - val_mae: 1.6557\n",
      "Epoch 159/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4077 - mae: 0.8842 - val_loss: 4.8619 - val_mae: 1.6551\n",
      "Epoch 160/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4035 - mae: 0.8846 - val_loss: 4.8568 - val_mae: 1.6540\n",
      "Epoch 161/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4085 - mae: 0.8770 - val_loss: 4.8537 - val_mae: 1.6541\n",
      "Epoch 162/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.4004 - mae: 0.8800 - val_loss: 4.8504 - val_mae: 1.6541\n",
      "Epoch 163/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.4028 - mae: 0.8732 - val_loss: 4.8444 - val_mae: 1.6532\n",
      "Epoch 164/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3959 - mae: 0.8750 - val_loss: 4.8420 - val_mae: 1.6532\n",
      "Epoch 165/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3922 - mae: 0.8820 - val_loss: 4.8415 - val_mae: 1.6532\n",
      "Epoch 166/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3950 - mae: 0.8738 - val_loss: 4.8358 - val_mae: 1.6520\n",
      "Epoch 167/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3889 - mae: 0.8798 - val_loss: 4.8332 - val_mae: 1.6523\n",
      "Epoch 168/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3896 - mae: 0.8717 - val_loss: 4.8297 - val_mae: 1.6515\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 169/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3892 - mae: 0.8684 - val_loss: 4.8260 - val_mae: 1.6511\n",
      "Epoch 170/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3856 - mae: 0.8669 - val_loss: 4.8224 - val_mae: 1.6512\n",
      "Epoch 171/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3854 - mae: 0.8672 - val_loss: 4.8194 - val_mae: 1.6516\n",
      "Epoch 172/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3789 - mae: 0.8694 - val_loss: 4.8186 - val_mae: 1.6515\n",
      "Epoch 173/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3767 - mae: 0.8701 - val_loss: 4.8149 - val_mae: 1.6504\n",
      "Epoch 174/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3815 - mae: 0.8575 - val_loss: 4.8118 - val_mae: 1.6498\n",
      "Epoch 175/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3752 - mae: 0.8601 - val_loss: 4.8087 - val_mae: 1.6495\n",
      "Epoch 176/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3684 - mae: 0.8627 - val_loss: 4.8075 - val_mae: 1.6489\n",
      "Epoch 177/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3703 - mae: 0.8606 - val_loss: 4.8033 - val_mae: 1.6484\n",
      "Epoch 178/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3696 - mae: 0.8560 - val_loss: 4.7993 - val_mae: 1.6478\n",
      "Epoch 179/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3668 - mae: 0.8606 - val_loss: 4.7951 - val_mae: 1.6473\n",
      "Epoch 180/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3675 - mae: 0.8510 - val_loss: 4.7898 - val_mae: 1.6466\n",
      "Epoch 181/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3644 - mae: 0.8547 - val_loss: 4.7878 - val_mae: 1.6464\n",
      "Epoch 182/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3643 - mae: 0.8489 - val_loss: 4.7836 - val_mae: 1.6465\n",
      "Epoch 183/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3600 - mae: 0.8522 - val_loss: 4.7798 - val_mae: 1.6463\n",
      "Epoch 184/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3560 - mae: 0.8558 - val_loss: 4.7767 - val_mae: 1.6458\n",
      "Epoch 185/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3550 - mae: 0.8509 - val_loss: 4.7722 - val_mae: 1.6456\n",
      "Epoch 186/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3521 - mae: 0.8534 - val_loss: 4.7683 - val_mae: 1.6445\n",
      "Epoch 187/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3497 - mae: 0.8507 - val_loss: 4.7661 - val_mae: 1.6437\n",
      "Epoch 188/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3525 - mae: 0.8465 - val_loss: 4.7617 - val_mae: 1.6427\n",
      "Epoch 189/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3461 - mae: 0.8457 - val_loss: 4.7600 - val_mae: 1.6424\n",
      "Epoch 190/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3466 - mae: 0.8458 - val_loss: 4.7557 - val_mae: 1.6422\n",
      "Epoch 191/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3413 - mae: 0.8479 - val_loss: 4.7540 - val_mae: 1.6423\n",
      "Epoch 192/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3436 - mae: 0.8435 - val_loss: 4.7511 - val_mae: 1.6425\n",
      "Epoch 193/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3383 - mae: 0.8427 - val_loss: 4.7501 - val_mae: 1.6429\n",
      "Epoch 194/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3414 - mae: 0.8416 - val_loss: 4.7480 - val_mae: 1.6423\n",
      "Epoch 195/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3392 - mae: 0.8388 - val_loss: 4.7452 - val_mae: 1.6422\n",
      "Epoch 196/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3326 - mae: 0.8435 - val_loss: 4.7433 - val_mae: 1.6414\n",
      "Epoch 197/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3409 - mae: 0.8331 - val_loss: 4.7422 - val_mae: 1.6410\n",
      "Epoch 198/500\n",
      "14/14 [==============================] - ETA: 0s - loss: 0.5557 - mae: 0.745 - 0s 2ms/step - loss: 1.3298 - mae: 0.8382 - val_loss: 4.7396 - val_mae: 1.6409\n",
      "Epoch 199/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3342 - mae: 0.8310 - val_loss: 4.7341 - val_mae: 1.6398\n",
      "Epoch 200/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3303 - mae: 0.8316 - val_loss: 4.7322 - val_mae: 1.6396\n",
      "Epoch 201/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3264 - mae: 0.8309 - val_loss: 4.7293 - val_mae: 1.6397\n",
      "Epoch 202/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3256 - mae: 0.8298 - val_loss: 4.7254 - val_mae: 1.6395\n",
      "Epoch 203/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3166 - mae: 0.8340 - val_loss: 4.7246 - val_mae: 1.6387\n",
      "Epoch 204/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3218 - mae: 0.8275 - val_loss: 4.7227 - val_mae: 1.6384\n",
      "Epoch 205/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3190 - mae: 0.8279 - val_loss: 4.7185 - val_mae: 1.6378\n",
      "Epoch 206/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3213 - mae: 0.8237 - val_loss: 4.7170 - val_mae: 1.6377\n",
      "Epoch 207/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3148 - mae: 0.8284 - val_loss: 4.7152 - val_mae: 1.6377\n",
      "Epoch 208/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3104 - mae: 0.8274 - val_loss: 4.7131 - val_mae: 1.6377\n",
      "Epoch 209/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3160 - mae: 0.8237 - val_loss: 4.7094 - val_mae: 1.6376\n",
      "Epoch 210/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3062 - mae: 0.8273 - val_loss: 4.7062 - val_mae: 1.6376\n",
      "Epoch 211/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3098 - mae: 0.8226 - val_loss: 4.7016 - val_mae: 1.6365\n",
      "Epoch 212/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3073 - mae: 0.8195 - val_loss: 4.6998 - val_mae: 1.6371\n",
      "Epoch 213/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3007 - mae: 0.8248 - val_loss: 4.6966 - val_mae: 1.6371\n",
      "Epoch 214/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.3025 - mae: 0.8179 - val_loss: 4.6941 - val_mae: 1.6375\n",
      "Epoch 215/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2969 - mae: 0.8235 - val_loss: 4.6917 - val_mae: 1.6373\n",
      "Epoch 216/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.3001 - mae: 0.8222 - val_loss: 4.6883 - val_mae: 1.6362\n",
      "Epoch 217/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2909 - mae: 0.8241 - val_loss: 4.6884 - val_mae: 1.6355\n",
      "Epoch 218/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2951 - mae: 0.8149 - val_loss: 4.6833 - val_mae: 1.6349\n",
      "Epoch 219/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2898 - mae: 0.8167 - val_loss: 4.6800 - val_mae: 1.6346\n",
      "Epoch 220/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2904 - mae: 0.8191 - val_loss: 4.6784 - val_mae: 1.6351\n",
      "Epoch 221/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2875 - mae: 0.8144 - val_loss: 4.6777 - val_mae: 1.6354\n",
      "Epoch 222/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2826 - mae: 0.8179 - val_loss: 4.6769 - val_mae: 1.6347\n",
      "Epoch 223/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2820 - mae: 0.8141 - val_loss: 4.6750 - val_mae: 1.6346\n",
      "Epoch 224/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2861 - mae: 0.8084 - val_loss: 4.6721 - val_mae: 1.6343\n",
      "Epoch 225/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2817 - mae: 0.8094 - val_loss: 4.6686 - val_mae: 1.6345\n",
      "Epoch 226/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2806 - mae: 0.8067 - val_loss: 4.6654 - val_mae: 1.6333\n",
      "Epoch 227/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2762 - mae: 0.8089 - val_loss: 4.6631 - val_mae: 1.6326\n",
      "Epoch 228/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2785 - mae: 0.8058 - val_loss: 4.6621 - val_mae: 1.6330\n",
      "Epoch 229/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2766 - mae: 0.8040 - val_loss: 4.6612 - val_mae: 1.6328\n",
      "Epoch 230/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2697 - mae: 0.8087 - val_loss: 4.6598 - val_mae: 1.6331\n",
      "Epoch 231/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2639 - mae: 0.8040 - val_loss: 4.6579 - val_mae: 1.6321\n",
      "Epoch 232/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2760 - mae: 0.7987 - val_loss: 4.6548 - val_mae: 1.6318\n",
      "Epoch 233/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2658 - mae: 0.8004 - val_loss: 4.6533 - val_mae: 1.6322\n",
      "Epoch 234/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2617 - mae: 0.7998 - val_loss: 4.6512 - val_mae: 1.6315\n",
      "Epoch 235/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2606 - mae: 0.8001 - val_loss: 4.6473 - val_mae: 1.6307\n",
      "Epoch 236/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2628 - mae: 0.7972 - val_loss: 4.6462 - val_mae: 1.6309\n",
      "Epoch 237/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2608 - mae: 0.7884 - val_loss: 4.6458 - val_mae: 1.6313\n",
      "Epoch 238/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2548 - mae: 0.7953 - val_loss: 4.6442 - val_mae: 1.6309\n",
      "Epoch 239/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2521 - mae: 0.7938 - val_loss: 4.6420 - val_mae: 1.6303\n",
      "Epoch 240/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2543 - mae: 0.7926 - val_loss: 4.6409 - val_mae: 1.6305\n",
      "Epoch 241/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2502 - mae: 0.7893 - val_loss: 4.6384 - val_mae: 1.6301\n",
      "Epoch 242/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2510 - mae: 0.7868 - val_loss: 4.6349 - val_mae: 1.6304\n",
      "Epoch 243/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2424 - mae: 0.7926 - val_loss: 4.6315 - val_mae: 1.6293\n",
      "Epoch 244/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2442 - mae: 0.7841 - val_loss: 4.6321 - val_mae: 1.6298\n",
      "Epoch 245/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2416 - mae: 0.7888 - val_loss: 4.6296 - val_mae: 1.6297\n",
      "Epoch 246/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2412 - mae: 0.7861 - val_loss: 4.6271 - val_mae: 1.6298\n",
      "Epoch 247/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2373 - mae: 0.7854 - val_loss: 4.6267 - val_mae: 1.6306\n",
      "Epoch 248/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2345 - mae: 0.7841 - val_loss: 4.6252 - val_mae: 1.6302\n",
      "Epoch 249/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2380 - mae: 0.7775 - val_loss: 4.6242 - val_mae: 1.6301\n",
      "Epoch 250/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2316 - mae: 0.7789 - val_loss: 4.6216 - val_mae: 1.6300\n",
      "Epoch 251/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2330 - mae: 0.7775 - val_loss: 4.6186 - val_mae: 1.6294\n",
      "Epoch 252/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2331 - mae: 0.7734 - val_loss: 4.6186 - val_mae: 1.6304\n",
      "Epoch 253/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2244 - mae: 0.7725 - val_loss: 4.6153 - val_mae: 1.6304\n",
      "Epoch 254/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2203 - mae: 0.7761 - val_loss: 4.6140 - val_mae: 1.6298\n",
      "Epoch 255/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2251 - mae: 0.7739 - val_loss: 4.6113 - val_mae: 1.6296\n",
      "Epoch 256/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2209 - mae: 0.7708 - val_loss: 4.6069 - val_mae: 1.6297\n",
      "Epoch 257/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2149 - mae: 0.7740 - val_loss: 4.6058 - val_mae: 1.6299\n",
      "Epoch 258/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2117 - mae: 0.7722 - val_loss: 4.6046 - val_mae: 1.6293\n",
      "Epoch 259/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2130 - mae: 0.7719 - val_loss: 4.6040 - val_mae: 1.6302\n",
      "Epoch 260/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2060 - mae: 0.7720 - val_loss: 4.6036 - val_mae: 1.6300\n",
      "Epoch 261/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.2076 - mae: 0.7685 - val_loss: 4.6048 - val_mae: 1.6299\n",
      "Epoch 262/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2160 - mae: 0.7612 - val_loss: 4.6024 - val_mae: 1.6298\n",
      "Epoch 263/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2026 - mae: 0.7630 - val_loss: 4.6028 - val_mae: 1.6301\n",
      "Epoch 264/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2069 - mae: 0.7606 - val_loss: 4.6023 - val_mae: 1.6308\n",
      "Epoch 265/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2050 - mae: 0.7595 - val_loss: 4.5978 - val_mae: 1.6305\n",
      "Epoch 266/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.2010 - mae: 0.7551 - val_loss: 4.5970 - val_mae: 1.6315\n",
      "Epoch 267/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1950 - mae: 0.7601 - val_loss: 4.5962 - val_mae: 1.6310\n",
      "Epoch 268/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1942 - mae: 0.7649 - val_loss: 4.5939 - val_mae: 1.6315\n",
      "Epoch 269/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1918 - mae: 0.7604 - val_loss: 4.5919 - val_mae: 1.6307\n",
      "Epoch 270/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1879 - mae: 0.7552 - val_loss: 4.5911 - val_mae: 1.6304\n",
      "Epoch 271/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1886 - mae: 0.7564 - val_loss: 4.5895 - val_mae: 1.6303\n",
      "Epoch 272/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1822 - mae: 0.7541 - val_loss: 4.5883 - val_mae: 1.6295\n",
      "Epoch 273/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1872 - mae: 0.7476 - val_loss: 4.5869 - val_mae: 1.6291\n",
      "Epoch 274/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1805 - mae: 0.7519 - val_loss: 4.5872 - val_mae: 1.6301\n",
      "Epoch 275/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1825 - mae: 0.7439 - val_loss: 4.5870 - val_mae: 1.6304\n",
      "Epoch 276/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1766 - mae: 0.7501 - val_loss: 4.5861 - val_mae: 1.6299\n",
      "Epoch 277/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1800 - mae: 0.7418 - val_loss: 4.5858 - val_mae: 1.6308\n",
      "Epoch 278/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1719 - mae: 0.7455 - val_loss: 4.5848 - val_mae: 1.6305\n",
      "Epoch 279/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1699 - mae: 0.7415 - val_loss: 4.5836 - val_mae: 1.6303\n",
      "Epoch 280/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1699 - mae: 0.7399 - val_loss: 4.5850 - val_mae: 1.6314\n",
      "Epoch 281/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1667 - mae: 0.7386 - val_loss: 4.5858 - val_mae: 1.6315\n",
      "Epoch 282/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1649 - mae: 0.7411 - val_loss: 4.5857 - val_mae: 1.6324\n",
      "Epoch 283/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1606 - mae: 0.7404 - val_loss: 4.5863 - val_mae: 1.6323\n",
      "Epoch 284/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1605 - mae: 0.7370 - val_loss: 4.5837 - val_mae: 1.6313\n",
      "Epoch 285/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1626 - mae: 0.7322 - val_loss: 4.5834 - val_mae: 1.6321\n",
      "Epoch 286/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1594 - mae: 0.7262 - val_loss: 4.5820 - val_mae: 1.6321\n",
      "Epoch 287/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1589 - mae: 0.7295 - val_loss: 4.5816 - val_mae: 1.6321\n",
      "Epoch 288/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1493 - mae: 0.7348 - val_loss: 4.5832 - val_mae: 1.6331\n",
      "Epoch 289/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1555 - mae: 0.7253 - val_loss: 4.5815 - val_mae: 1.6326\n",
      "Epoch 290/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1472 - mae: 0.7275 - val_loss: 4.5820 - val_mae: 1.6343\n",
      "Epoch 291/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1455 - mae: 0.7289 - val_loss: 4.5817 - val_mae: 1.6342\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 292/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1387 - mae: 0.7284 - val_loss: 4.5827 - val_mae: 1.6353\n",
      "Epoch 293/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1401 - mae: 0.7280 - val_loss: 4.5812 - val_mae: 1.6348\n",
      "Epoch 294/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1326 - mae: 0.7308 - val_loss: 4.5798 - val_mae: 1.6342\n",
      "Epoch 295/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1375 - mae: 0.7242 - val_loss: 4.5799 - val_mae: 1.6349\n",
      "Epoch 296/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1267 - mae: 0.7292 - val_loss: 4.5829 - val_mae: 1.6359\n",
      "Epoch 297/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1345 - mae: 0.7211 - val_loss: 4.5824 - val_mae: 1.6364\n",
      "Epoch 298/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1247 - mae: 0.7212 - val_loss: 4.5832 - val_mae: 1.6363\n",
      "Epoch 299/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1324 - mae: 0.7139 - val_loss: 4.5854 - val_mae: 1.6373\n",
      "Epoch 300/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1236 - mae: 0.7182 - val_loss: 4.5878 - val_mae: 1.6388\n",
      "Epoch 301/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1223 - mae: 0.7132 - val_loss: 4.5869 - val_mae: 1.6386\n",
      "Epoch 302/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1139 - mae: 0.7228 - val_loss: 4.5899 - val_mae: 1.6398\n",
      "Epoch 303/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1179 - mae: 0.7188 - val_loss: 4.5900 - val_mae: 1.6402\n",
      "Epoch 304/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1100 - mae: 0.7172 - val_loss: 4.5927 - val_mae: 1.6414\n",
      "Epoch 305/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1070 - mae: 0.7177 - val_loss: 4.5911 - val_mae: 1.6399\n",
      "Epoch 306/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1113 - mae: 0.7119 - val_loss: 4.5929 - val_mae: 1.6409\n",
      "Epoch 307/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1028 - mae: 0.7195 - val_loss: 4.5933 - val_mae: 1.6405\n",
      "Epoch 308/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.1035 - mae: 0.7163 - val_loss: 4.5951 - val_mae: 1.6416\n",
      "Epoch 309/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.1050 - mae: 0.7077 - val_loss: 4.5949 - val_mae: 1.6410\n",
      "Epoch 310/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0991 - mae: 0.7107 - val_loss: 4.5944 - val_mae: 1.6411\n",
      "Epoch 311/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0956 - mae: 0.7114 - val_loss: 4.5974 - val_mae: 1.6432\n",
      "Epoch 312/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0916 - mae: 0.7055 - val_loss: 4.5977 - val_mae: 1.6427\n",
      "Epoch 313/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0928 - mae: 0.7053 - val_loss: 4.6003 - val_mae: 1.6438\n",
      "Epoch 314/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0858 - mae: 0.7041 - val_loss: 4.6002 - val_mae: 1.6438\n",
      "Epoch 315/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0881 - mae: 0.7019 - val_loss: 4.6030 - val_mae: 1.6449\n",
      "Epoch 316/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0871 - mae: 0.7010 - val_loss: 4.6034 - val_mae: 1.6458\n",
      "Epoch 317/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0858 - mae: 0.6925 - val_loss: 4.6033 - val_mae: 1.6456\n",
      "Epoch 318/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0764 - mae: 0.6987 - val_loss: 4.6060 - val_mae: 1.6471\n",
      "Epoch 319/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0740 - mae: 0.6984 - val_loss: 4.6074 - val_mae: 1.6477\n",
      "Epoch 320/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0684 - mae: 0.7029 - val_loss: 4.6085 - val_mae: 1.6474\n",
      "Epoch 321/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0701 - mae: 0.7042 - val_loss: 4.6124 - val_mae: 1.6491\n",
      "Epoch 322/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0658 - mae: 0.6996 - val_loss: 4.6130 - val_mae: 1.6488\n",
      "Epoch 323/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.0684 - mae: 0.6948 - val_loss: 4.6184 - val_mae: 1.6511\n",
      "Epoch 324/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.0596 - mae: 0.6997 - val_loss: 4.6189 - val_mae: 1.6509\n",
      "Epoch 325/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0590 - mae: 0.6978 - val_loss: 4.6227 - val_mae: 1.6521\n",
      "Epoch 326/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0666 - mae: 0.6855 - val_loss: 4.6227 - val_mae: 1.6520\n",
      "Epoch 327/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0559 - mae: 0.6876 - val_loss: 4.6258 - val_mae: 1.6534\n",
      "Epoch 328/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0535 - mae: 0.6865 - val_loss: 4.6296 - val_mae: 1.6542\n",
      "Epoch 329/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0559 - mae: 0.6810 - val_loss: 4.6310 - val_mae: 1.6552\n",
      "Epoch 330/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0448 - mae: 0.6875 - val_loss: 4.6330 - val_mae: 1.6555\n",
      "Epoch 331/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0469 - mae: 0.6793 - val_loss: 4.6340 - val_mae: 1.6557\n",
      "Epoch 332/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0384 - mae: 0.6830 - val_loss: 4.6391 - val_mae: 1.6573\n",
      "Epoch 333/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0416 - mae: 0.6866 - val_loss: 4.6412 - val_mae: 1.6586\n",
      "Epoch 334/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0420 - mae: 0.6789 - val_loss: 4.6411 - val_mae: 1.6585\n",
      "Epoch 335/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0299 - mae: 0.6829 - val_loss: 4.6422 - val_mae: 1.6583\n",
      "Epoch 336/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0296 - mae: 0.6873 - val_loss: 4.6484 - val_mae: 1.6604\n",
      "Epoch 337/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0337 - mae: 0.6754 - val_loss: 4.6496 - val_mae: 1.6607\n",
      "Epoch 338/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0263 - mae: 0.6782 - val_loss: 4.6556 - val_mae: 1.6631\n",
      "Epoch 339/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0174 - mae: 0.6711 - val_loss: 4.6601 - val_mae: 1.6645\n",
      "Epoch 340/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0236 - mae: 0.6720 - val_loss: 4.6619 - val_mae: 1.6647\n",
      "Epoch 341/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0228 - mae: 0.6716 - val_loss: 4.6634 - val_mae: 1.6650\n",
      "Epoch 342/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0248 - mae: 0.6665 - val_loss: 4.6679 - val_mae: 1.6668\n",
      "Epoch 343/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0145 - mae: 0.6710 - val_loss: 4.6694 - val_mae: 1.6675\n",
      "Epoch 344/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0207 - mae: 0.6628 - val_loss: 4.6707 - val_mae: 1.6671\n",
      "Epoch 345/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0085 - mae: 0.6657 - val_loss: 4.6783 - val_mae: 1.6695\n",
      "Epoch 346/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0120 - mae: 0.6616 - val_loss: 4.6830 - val_mae: 1.6713\n",
      "Epoch 347/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0084 - mae: 0.6610 - val_loss: 4.6860 - val_mae: 1.6717\n",
      "Epoch 348/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9971 - mae: 0.6620 - val_loss: 4.6930 - val_mae: 1.6741\n",
      "Epoch 349/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0085 - mae: 0.6550 - val_loss: 4.6921 - val_mae: 1.6737\n",
      "Epoch 350/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.0022 - mae: 0.6592 - val_loss: 4.6928 - val_mae: 1.6735\n",
      "Epoch 351/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9950 - mae: 0.6629 - val_loss: 4.6999 - val_mae: 1.6754\n",
      "Epoch 352/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9968 - mae: 0.6534 - val_loss: 4.7063 - val_mae: 1.6778\n",
      "Epoch 353/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9980 - mae: 0.6515 - val_loss: 4.7101 - val_mae: 1.6783\n",
      "Epoch 354/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9767 - mae: 0.6554 - val_loss: 4.7133 - val_mae: 1.6781\n",
      "Epoch 355/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9940 - mae: 0.6535 - val_loss: 4.7170 - val_mae: 1.6797\n",
      "Epoch 356/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9890 - mae: 0.6496 - val_loss: 4.7205 - val_mae: 1.6807\n",
      "Epoch 357/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9819 - mae: 0.6544 - val_loss: 4.7232 - val_mae: 1.6819\n",
      "Epoch 358/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9859 - mae: 0.6347 - val_loss: 4.7300 - val_mae: 1.6847\n",
      "Epoch 359/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9748 - mae: 0.6525 - val_loss: 4.7307 - val_mae: 1.6845\n",
      "Epoch 360/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9771 - mae: 0.6492 - val_loss: 4.7330 - val_mae: 1.6846\n",
      "Epoch 361/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9679 - mae: 0.6552 - val_loss: 4.7417 - val_mae: 1.6868\n",
      "Epoch 362/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9608 - mae: 0.6473 - val_loss: 4.7460 - val_mae: 1.6873\n",
      "Epoch 363/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9658 - mae: 0.6536 - val_loss: 4.7525 - val_mae: 1.6883\n",
      "Epoch 364/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9642 - mae: 0.6436 - val_loss: 4.7535 - val_mae: 1.6881\n",
      "Epoch 365/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9717 - mae: 0.6351 - val_loss: 4.7599 - val_mae: 1.6897\n",
      "Epoch 366/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9545 - mae: 0.6432 - val_loss: 4.7656 - val_mae: 1.6902\n",
      "Epoch 367/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9603 - mae: 0.6377 - val_loss: 4.7670 - val_mae: 1.6904\n",
      "Epoch 368/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9622 - mae: 0.6299 - val_loss: 4.7746 - val_mae: 1.6933\n",
      "Epoch 369/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9509 - mae: 0.6349 - val_loss: 4.7763 - val_mae: 1.6929\n",
      "Epoch 370/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9534 - mae: 0.6328 - val_loss: 4.7855 - val_mae: 1.6960\n",
      "Epoch 371/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9528 - mae: 0.6299 - val_loss: 4.7882 - val_mae: 1.6963\n",
      "Epoch 372/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9415 - mae: 0.6318 - val_loss: 4.7981 - val_mae: 1.6987\n",
      "Epoch 373/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9495 - mae: 0.6260 - val_loss: 4.8009 - val_mae: 1.6988\n",
      "Epoch 374/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9422 - mae: 0.6356 - val_loss: 4.8064 - val_mae: 1.7004\n",
      "Epoch 375/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9446 - mae: 0.6228 - val_loss: 4.8104 - val_mae: 1.7008\n",
      "Epoch 376/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9368 - mae: 0.6318 - val_loss: 4.8185 - val_mae: 1.7029\n",
      "Epoch 377/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9329 - mae: 0.6231 - val_loss: 4.8247 - val_mae: 1.7047\n",
      "Epoch 378/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9299 - mae: 0.6321 - val_loss: 4.8288 - val_mae: 1.7047\n",
      "Epoch 379/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9357 - mae: 0.6250 - val_loss: 4.8375 - val_mae: 1.7069\n",
      "Epoch 380/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9277 - mae: 0.6289 - val_loss: 4.8396 - val_mae: 1.7068\n",
      "Epoch 381/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9231 - mae: 0.6226 - val_loss: 4.8493 - val_mae: 1.7092\n",
      "Epoch 382/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9292 - mae: 0.6254 - val_loss: 4.8533 - val_mae: 1.7092\n",
      "Epoch 383/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9233 - mae: 0.6303 - val_loss: 4.8637 - val_mae: 1.7111\n",
      "Epoch 384/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9262 - mae: 0.6224 - val_loss: 4.8679 - val_mae: 1.7116\n",
      "Epoch 385/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9313 - mae: 0.6090 - val_loss: 4.8759 - val_mae: 1.7135\n",
      "Epoch 386/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9235 - mae: 0.6145 - val_loss: 4.8792 - val_mae: 1.7132\n",
      "Epoch 387/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9103 - mae: 0.6161 - val_loss: 4.8900 - val_mae: 1.7149\n",
      "Epoch 388/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9271 - mae: 0.6017 - val_loss: 4.8960 - val_mae: 1.7164\n",
      "Epoch 389/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9152 - mae: 0.6151 - val_loss: 4.8999 - val_mae: 1.7168\n",
      "Epoch 390/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9178 - mae: 0.6046 - val_loss: 4.9076 - val_mae: 1.7184\n",
      "Epoch 391/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9054 - mae: 0.6104 - val_loss: 4.9121 - val_mae: 1.7186\n",
      "Epoch 392/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9208 - mae: 0.6031 - val_loss: 4.9201 - val_mae: 1.7204\n",
      "Epoch 393/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9051 - mae: 0.6071 - val_loss: 4.9243 - val_mae: 1.7207\n",
      "Epoch 394/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9022 - mae: 0.6027 - val_loss: 4.9347 - val_mae: 1.7228\n",
      "Epoch 395/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9208 - mae: 0.5955 - val_loss: 4.9435 - val_mae: 1.7245\n",
      "Epoch 396/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9042 - mae: 0.5979 - val_loss: 4.9467 - val_mae: 1.7246\n",
      "Epoch 397/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8992 - mae: 0.6056 - val_loss: 4.9557 - val_mae: 1.7263\n",
      "Epoch 398/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9001 - mae: 0.5985 - val_loss: 4.9593 - val_mae: 1.7264\n",
      "Epoch 399/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8923 - mae: 0.6023 - val_loss: 4.9694 - val_mae: 1.7283\n",
      "Epoch 400/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.9059 - mae: 0.5887 - val_loss: 4.9755 - val_mae: 1.7287\n",
      "Epoch 401/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8918 - mae: 0.5882 - val_loss: 4.9783 - val_mae: 1.7288\n",
      "Epoch 402/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8869 - mae: 0.6002 - val_loss: 4.9864 - val_mae: 1.7303\n",
      "Epoch 403/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8898 - mae: 0.5930 - val_loss: 4.9921 - val_mae: 1.7308\n",
      "Epoch 404/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8850 - mae: 0.5885 - val_loss: 5.0038 - val_mae: 1.7337\n",
      "Epoch 405/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8822 - mae: 0.5912 - val_loss: 5.0068 - val_mae: 1.7337\n",
      "Epoch 406/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8827 - mae: 0.6022 - val_loss: 5.0185 - val_mae: 1.7363\n",
      "Epoch 407/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8736 - mae: 0.5990 - val_loss: 5.0282 - val_mae: 1.7382\n",
      "Epoch 408/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8894 - mae: 0.5869 - val_loss: 5.0321 - val_mae: 1.7383\n",
      "Epoch 409/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8759 - mae: 0.6030 - val_loss: 5.0418 - val_mae: 1.7400\n",
      "Epoch 410/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8803 - mae: 0.5792 - val_loss: 5.0498 - val_mae: 1.7418\n",
      "Epoch 411/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8642 - mae: 0.5973 - val_loss: 5.0533 - val_mae: 1.7409\n",
      "Epoch 412/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8907 - mae: 0.5742 - val_loss: 5.0583 - val_mae: 1.7419\n",
      "Epoch 413/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8782 - mae: 0.5759 - val_loss: 5.0606 - val_mae: 1.7417\n",
      "Epoch 414/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8784 - mae: 0.5788 - val_loss: 5.0715 - val_mae: 1.7437\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 415/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8648 - mae: 0.5799 - val_loss: 5.0821 - val_mae: 1.7463\n",
      "Epoch 416/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8780 - mae: 0.5732 - val_loss: 5.0859 - val_mae: 1.7463\n",
      "Epoch 417/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8690 - mae: 0.5798 - val_loss: 5.0977 - val_mae: 1.7486\n",
      "Epoch 418/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8602 - mae: 0.5786 - val_loss: 5.1012 - val_mae: 1.7479\n",
      "Epoch 419/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8645 - mae: 0.5801 - val_loss: 5.1112 - val_mae: 1.7499\n",
      "Epoch 420/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8623 - mae: 0.5712 - val_loss: 5.1152 - val_mae: 1.7492\n",
      "Epoch 421/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8618 - mae: 0.5747 - val_loss: 5.1267 - val_mae: 1.7515\n",
      "Epoch 422/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8518 - mae: 0.5726 - val_loss: 5.1417 - val_mae: 1.7545\n",
      "Epoch 423/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8600 - mae: 0.5783 - val_loss: 5.1455 - val_mae: 1.7548\n",
      "Epoch 424/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8594 - mae: 0.5726 - val_loss: 5.1573 - val_mae: 1.7573\n",
      "Epoch 425/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8445 - mae: 0.5747 - val_loss: 5.1621 - val_mae: 1.7570\n",
      "Epoch 426/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8591 - mae: 0.5707 - val_loss: 5.1727 - val_mae: 1.7596\n",
      "Epoch 427/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8500 - mae: 0.5658 - val_loss: 5.1851 - val_mae: 1.7621\n",
      "Epoch 428/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8424 - mae: 0.5762 - val_loss: 5.1894 - val_mae: 1.7616\n",
      "Epoch 429/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8625 - mae: 0.5577 - val_loss: 5.1986 - val_mae: 1.7637\n",
      "Epoch 430/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 0.8422 - mae: 0.5717 - val_loss: 5.2003 - val_mae: 1.7625\n",
      "Epoch 431/500\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 0.8448 - mae: 0.5590 - val_loss: 5.2124 - val_mae: 1.7653\n",
      "Epoch 432/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8398 - mae: 0.5666 - val_loss: 5.2177 - val_mae: 1.7646\n",
      "Epoch 433/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8357 - mae: 0.5628 - val_loss: 5.2318 - val_mae: 1.7672\n",
      "Epoch 434/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8486 - mae: 0.5651 - val_loss: 5.2427 - val_mae: 1.7695\n",
      "Epoch 435/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8487 - mae: 0.5466 - val_loss: 5.2446 - val_mae: 1.7691\n",
      "Epoch 436/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8302 - mae: 0.5601 - val_loss: 5.2574 - val_mae: 1.7712\n",
      "Epoch 437/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8453 - mae: 0.5526 - val_loss: 5.2676 - val_mae: 1.7730\n",
      "Epoch 438/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8378 - mae: 0.5445 - val_loss: 5.2709 - val_mae: 1.7729\n",
      "Epoch 439/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8390 - mae: 0.5527 - val_loss: 5.2834 - val_mae: 1.7751\n",
      "Epoch 440/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8287 - mae: 0.5568 - val_loss: 5.2859 - val_mae: 1.7744\n",
      "Epoch 441/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8223 - mae: 0.5538 - val_loss: 5.2975 - val_mae: 1.7758\n",
      "Epoch 442/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8341 - mae: 0.5527 - val_loss: 5.3094 - val_mae: 1.7783\n",
      "Epoch 443/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8362 - mae: 0.5483 - val_loss: 5.3104 - val_mae: 1.7779\n",
      "Epoch 444/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8188 - mae: 0.5524 - val_loss: 5.3239 - val_mae: 1.7805\n",
      "Epoch 445/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8333 - mae: 0.5386 - val_loss: 5.3359 - val_mae: 1.7837\n",
      "Epoch 446/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8157 - mae: 0.5477 - val_loss: 5.3492 - val_mae: 1.7859\n",
      "Epoch 447/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8282 - mae: 0.5464 - val_loss: 5.3511 - val_mae: 1.7852\n",
      "Epoch 448/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8127 - mae: 0.5455 - val_loss: 5.3621 - val_mae: 1.7870\n",
      "Epoch 449/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8244 - mae: 0.5463 - val_loss: 5.3660 - val_mae: 1.7864\n",
      "Epoch 450/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8185 - mae: 0.5444 - val_loss: 5.3811 - val_mae: 1.7898\n",
      "Epoch 451/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8102 - mae: 0.5432 - val_loss: 5.3878 - val_mae: 1.7899\n",
      "Epoch 452/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8293 - mae: 0.5364 - val_loss: 5.3992 - val_mae: 1.7929\n",
      "Epoch 453/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8220 - mae: 0.5386 - val_loss: 5.4115 - val_mae: 1.7955\n",
      "Epoch 454/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8106 - mae: 0.5396 - val_loss: 5.4151 - val_mae: 1.7950\n",
      "Epoch 455/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8120 - mae: 0.5329 - val_loss: 5.4289 - val_mae: 1.7977\n",
      "Epoch 456/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8202 - mae: 0.5272 - val_loss: 5.4387 - val_mae: 1.7997\n",
      "Epoch 457/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8034 - mae: 0.5378 - val_loss: 5.4526 - val_mae: 1.8023\n",
      "Epoch 458/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8211 - mae: 0.5316 - val_loss: 5.4506 - val_mae: 1.8004\n",
      "Epoch 459/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8077 - mae: 0.5259 - val_loss: 5.4621 - val_mae: 1.8030\n",
      "Epoch 460/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8007 - mae: 0.5336 - val_loss: 5.4771 - val_mae: 1.8057\n",
      "Epoch 461/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8025 - mae: 0.5367 - val_loss: 5.4887 - val_mae: 1.8073\n",
      "Epoch 462/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7984 - mae: 0.5347 - val_loss: 5.4935 - val_mae: 1.8068\n",
      "Epoch 463/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8108 - mae: 0.5255 - val_loss: 5.4936 - val_mae: 1.8060\n",
      "Epoch 464/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7950 - mae: 0.5267 - val_loss: 5.5028 - val_mae: 1.8076\n",
      "Epoch 465/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.8078 - mae: 0.5233 - val_loss: 5.5117 - val_mae: 1.8095\n",
      "Epoch 466/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7944 - mae: 0.5308 - val_loss: 5.5211 - val_mae: 1.8108\n",
      "Epoch 467/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7927 - mae: 0.5230 - val_loss: 5.5235 - val_mae: 1.8097\n",
      "Epoch 468/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.8085 - mae: 0.5277 - val_loss: 5.5318 - val_mae: 1.8116\n",
      "Epoch 469/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7934 - mae: 0.5216 - val_loss: 5.5425 - val_mae: 1.8142\n",
      "Epoch 470/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7903 - mae: 0.5273 - val_loss: 5.5464 - val_mae: 1.8136\n",
      "Epoch 471/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7873 - mae: 0.5231 - val_loss: 5.5580 - val_mae: 1.8155\n",
      "Epoch 472/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7958 - mae: 0.5236 - val_loss: 5.5694 - val_mae: 1.8178\n",
      "Epoch 473/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7896 - mae: 0.5255 - val_loss: 5.5752 - val_mae: 1.8173\n",
      "Epoch 474/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7907 - mae: 0.5119 - val_loss: 5.5859 - val_mae: 1.8192\n",
      "Epoch 475/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7967 - mae: 0.5072 - val_loss: 5.5972 - val_mae: 1.8216\n",
      "Epoch 476/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7826 - mae: 0.5112 - val_loss: 5.5991 - val_mae: 1.8211\n",
      "Epoch 477/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7778 - mae: 0.5186 - val_loss: 5.6090 - val_mae: 1.8227\n",
      "Epoch 478/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7896 - mae: 0.5151 - val_loss: 5.6236 - val_mae: 1.8258\n",
      "Epoch 479/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7741 - mae: 0.5146 - val_loss: 5.6331 - val_mae: 1.8274\n",
      "Epoch 480/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7847 - mae: 0.5189 - val_loss: 5.6330 - val_mae: 1.8254\n",
      "Epoch 481/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7873 - mae: 0.5152 - val_loss: 5.6462 - val_mae: 1.8285\n",
      "Epoch 482/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7703 - mae: 0.5133 - val_loss: 5.6492 - val_mae: 1.8274\n",
      "Epoch 483/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7883 - mae: 0.5159 - val_loss: 5.6605 - val_mae: 1.8300\n",
      "Epoch 484/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7845 - mae: 0.5025 - val_loss: 5.6711 - val_mae: 1.8318\n",
      "Epoch 485/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7735 - mae: 0.5004 - val_loss: 5.6833 - val_mae: 1.8340\n",
      "Epoch 486/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7780 - mae: 0.5033 - val_loss: 5.6940 - val_mae: 1.8357\n",
      "Epoch 487/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7651 - mae: 0.5085 - val_loss: 5.7051 - val_mae: 1.8369\n",
      "Epoch 488/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7772 - mae: 0.5047 - val_loss: 5.7067 - val_mae: 1.8363\n",
      "Epoch 489/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7694 - mae: 0.5001 - val_loss: 5.7187 - val_mae: 1.8389\n",
      "Epoch 490/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7592 - mae: 0.5000 - val_loss: 5.7197 - val_mae: 1.8374\n",
      "Epoch 491/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7732 - mae: 0.5079 - val_loss: 5.7321 - val_mae: 1.8396\n",
      "Epoch 492/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7678 - mae: 0.5077 - val_loss: 5.7497 - val_mae: 1.8430\n",
      "Epoch 493/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7654 - mae: 0.4999 - val_loss: 5.7650 - val_mae: 1.8463\n",
      "Epoch 494/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7701 - mae: 0.4954 - val_loss: 5.7764 - val_mae: 1.8486\n",
      "Epoch 495/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7577 - mae: 0.5024 - val_loss: 5.7831 - val_mae: 1.8483\n",
      "Epoch 496/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7695 - mae: 0.5002 - val_loss: 5.7962 - val_mae: 1.8507\n",
      "Epoch 497/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7616 - mae: 0.4909 - val_loss: 5.8079 - val_mae: 1.8530\n",
      "Epoch 498/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7618 - mae: 0.4936 - val_loss: 5.8066 - val_mae: 1.8505\n",
      "Epoch 499/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 0.7623 - mae: 0.4899 - val_loss: 5.8184 - val_mae: 1.8524\n",
      "Epoch 500/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 0.7617 - mae: 0.4896 - val_loss: 5.8349 - val_mae: 1.8561\n",
      "processing fold # 3\n",
      "Epoch 1/500\n",
      "14/14 [==============================] - 0s 14ms/step - loss: 5.8331 - mae: 1.9912 - val_loss: 13.6288 - val_mae: 3.1227\n",
      "Epoch 2/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.6734 - mae: 1.9542 - val_loss: 13.4481 - val_mae: 3.0960\n",
      "Epoch 3/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.5621 - mae: 1.9232 - val_loss: 13.2962 - val_mae: 3.0737\n",
      "Epoch 4/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.4834 - mae: 1.8992 - val_loss: 13.1310 - val_mae: 3.0498\n",
      "Epoch 5/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.4074 - mae: 1.8760 - val_loss: 12.9746 - val_mae: 3.0265\n",
      "Epoch 6/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.3348 - mae: 1.8525 - val_loss: 12.8232 - val_mae: 3.0041\n",
      "Epoch 7/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.2677 - mae: 1.8321 - val_loss: 12.6905 - val_mae: 2.9837\n",
      "Epoch 8/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.2095 - mae: 1.8138 - val_loss: 12.5518 - val_mae: 2.9629\n",
      "Epoch 9/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.1535 - mae: 1.7966 - val_loss: 12.4063 - val_mae: 2.9411\n",
      "Epoch 10/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.1009 - mae: 1.7788 - val_loss: 12.2741 - val_mae: 2.9211\n",
      "Epoch 11/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.0560 - mae: 1.7658 - val_loss: 12.1239 - val_mae: 2.8990\n",
      "Epoch 12/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 5.0108 - mae: 1.7513 - val_loss: 11.9808 - val_mae: 2.8779\n",
      "Epoch 13/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.9669 - mae: 1.7377 - val_loss: 11.8363 - val_mae: 2.8566\n",
      "Epoch 14/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.9205 - mae: 1.7238 - val_loss: 11.6832 - val_mae: 2.8339\n",
      "Epoch 15/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.8793 - mae: 1.7099 - val_loss: 11.5602 - val_mae: 2.8154\n",
      "Epoch 16/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.8398 - mae: 1.6984 - val_loss: 11.4200 - val_mae: 2.7944\n",
      "Epoch 17/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.7991 - mae: 1.6865 - val_loss: 11.2771 - val_mae: 2.7732\n",
      "Epoch 18/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.7617 - mae: 1.6742 - val_loss: 11.2051 - val_mae: 2.7613\n",
      "Epoch 19/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.7260 - mae: 1.6635 - val_loss: 11.1308 - val_mae: 2.7488\n",
      "Epoch 20/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.6884 - mae: 1.6525 - val_loss: 11.0537 - val_mae: 2.7357\n",
      "Epoch 21/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.6542 - mae: 1.6412 - val_loss: 10.9792 - val_mae: 2.7232\n",
      "Epoch 22/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.6191 - mae: 1.6305 - val_loss: 10.9047 - val_mae: 2.7105\n",
      "Epoch 23/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.5828 - mae: 1.6196 - val_loss: 10.8303 - val_mae: 2.6977\n",
      "Epoch 24/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.5466 - mae: 1.6092 - val_loss: 10.7529 - val_mae: 2.6844\n",
      "Epoch 25/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.5145 - mae: 1.5967 - val_loss: 10.6868 - val_mae: 2.6731\n",
      "Epoch 26/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.4819 - mae: 1.5874 - val_loss: 10.6143 - val_mae: 2.6603\n",
      "Epoch 27/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.4456 - mae: 1.5766 - val_loss: 10.5395 - val_mae: 2.6473\n",
      "Epoch 28/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.4142 - mae: 1.5648 - val_loss: 10.4758 - val_mae: 2.6361\n",
      "Epoch 29/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.3846 - mae: 1.5541 - val_loss: 10.4100 - val_mae: 2.6246\n",
      "Epoch 30/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.3487 - mae: 1.5449 - val_loss: 10.3309 - val_mae: 2.6105\n",
      "Epoch 31/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.3150 - mae: 1.5330 - val_loss: 10.2637 - val_mae: 2.5985\n",
      "Epoch 32/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.2836 - mae: 1.5228 - val_loss: 10.1975 - val_mae: 2.5866\n",
      "Epoch 33/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.2543 - mae: 1.5116 - val_loss: 10.1345 - val_mae: 2.5753\n",
      "Epoch 34/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.2237 - mae: 1.5020 - val_loss: 10.0672 - val_mae: 2.5631\n",
      "Epoch 35/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1942 - mae: 1.4904 - val_loss: 10.0059 - val_mae: 2.5521\n",
      "Epoch 36/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1634 - mae: 1.4816 - val_loss: 9.9379 - val_mae: 2.5396\n",
      "Epoch 37/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1319 - mae: 1.4711 - val_loss: 9.8731 - val_mae: 2.5276\n",
      "Epoch 38/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.1018 - mae: 1.4610 - val_loss: 9.8103 - val_mae: 2.5159\n",
      "Epoch 39/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.0737 - mae: 1.4497 - val_loss: 9.7566 - val_mae: 2.5056\n",
      "Epoch 40/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.0473 - mae: 1.4396 - val_loss: 9.7053 - val_mae: 2.4956\n",
      "Epoch 41/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 4.0186 - mae: 1.4305 - val_loss: 9.6461 - val_mae: 2.4841\n",
      "Epoch 42/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9903 - mae: 1.4196 - val_loss: 9.5923 - val_mae: 2.4736\n",
      "Epoch 43/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9639 - mae: 1.4095 - val_loss: 9.5390 - val_mae: 2.4632\n",
      "Epoch 44/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9355 - mae: 1.4007 - val_loss: 9.4822 - val_mae: 2.4520\n",
      "Epoch 45/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.9098 - mae: 1.3897 - val_loss: 9.4302 - val_mae: 2.4417\n",
      "Epoch 46/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8848 - mae: 1.3795 - val_loss: 9.3823 - val_mae: 2.4322\n",
      "Epoch 47/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8579 - mae: 1.3707 - val_loss: 9.3261 - val_mae: 2.4210\n",
      "Epoch 48/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8310 - mae: 1.3613 - val_loss: 9.2719 - val_mae: 2.4101\n",
      "Epoch 49/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.8052 - mae: 1.3508 - val_loss: 9.2228 - val_mae: 2.4002\n",
      "Epoch 50/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.7784 - mae: 1.3420 - val_loss: 9.1686 - val_mae: 2.3894\n",
      "Epoch 51/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.7524 - mae: 1.3325 - val_loss: 9.1215 - val_mae: 2.3795\n",
      "Epoch 52/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.7289 - mae: 1.3219 - val_loss: 9.0815 - val_mae: 2.3711\n",
      "Epoch 53/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.7047 - mae: 1.3138 - val_loss: 9.0377 - val_mae: 2.3618\n",
      "Epoch 54/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.6810 - mae: 1.3042 - val_loss: 8.9941 - val_mae: 2.3526\n",
      "Epoch 55/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.6575 - mae: 1.2949 - val_loss: 8.9537 - val_mae: 2.3440\n",
      "Epoch 56/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.6344 - mae: 1.2860 - val_loss: 8.9110 - val_mae: 2.3348\n",
      "Epoch 57/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.6122 - mae: 1.2770 - val_loss: 8.8709 - val_mae: 2.3262\n",
      "Epoch 58/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.5874 - mae: 1.2692 - val_loss: 8.8231 - val_mae: 2.3159\n",
      "Epoch 59/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.5634 - mae: 1.2597 - val_loss: 8.7817 - val_mae: 2.3070\n",
      "Epoch 60/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.5426 - mae: 1.2492 - val_loss: 8.7459 - val_mae: 2.2992\n",
      "Epoch 61/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.5214 - mae: 1.2417 - val_loss: 8.7029 - val_mae: 2.2898\n",
      "Epoch 62/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4988 - mae: 1.2322 - val_loss: 8.6635 - val_mae: 2.2812\n",
      "Epoch 63/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4771 - mae: 1.2243 - val_loss: 8.6217 - val_mae: 2.2720\n",
      "Epoch 64/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4557 - mae: 1.2148 - val_loss: 8.5832 - val_mae: 2.2635\n",
      "Epoch 65/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4334 - mae: 1.2069 - val_loss: 8.5405 - val_mae: 2.2541\n",
      "Epoch 66/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.4135 - mae: 1.1966 - val_loss: 8.5059 - val_mae: 2.2464\n",
      "Epoch 67/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3936 - mae: 1.1893 - val_loss: 8.4670 - val_mae: 2.2377\n",
      "Epoch 68/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3730 - mae: 1.1811 - val_loss: 8.4265 - val_mae: 2.2287\n",
      "Epoch 69/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3525 - mae: 1.1709 - val_loss: 8.3912 - val_mae: 2.2207\n",
      "Epoch 70/500\n",
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      "Epoch 71/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.3135 - mae: 1.1554 - val_loss: 8.3144 - val_mae: 2.2034\n",
      "Epoch 72/500\n",
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      "Epoch 73/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2750 - mae: 1.1373 - val_loss: 8.2430 - val_mae: 2.1871\n",
      "Epoch 74/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2562 - mae: 1.1294 - val_loss: 8.2060 - val_mae: 2.1786\n",
      "Epoch 75/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2359 - mae: 1.1222 - val_loss: 8.1654 - val_mae: 2.1693\n",
      "Epoch 76/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.2175 - mae: 1.1124 - val_loss: 8.1312 - val_mae: 2.1614\n",
      "Epoch 77/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1983 - mae: 1.1051 - val_loss: 8.0936 - val_mae: 2.1527\n",
      "Epoch 78/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1820 - mae: 1.0947 - val_loss: 8.0643 - val_mae: 2.1459\n",
      "Epoch 79/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1644 - mae: 1.0886 - val_loss: 8.0255 - val_mae: 2.1368\n",
      "Epoch 80/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1445 - mae: 1.0809 - val_loss: 7.9878 - val_mae: 2.1280\n",
      "Epoch 81/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1278 - mae: 1.0708 - val_loss: 7.9577 - val_mae: 2.1209\n",
      "Epoch 82/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.1117 - mae: 1.0631 - val_loss: 7.9245 - val_mae: 2.1131\n",
      "Epoch 83/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0950 - mae: 1.0563 - val_loss: 7.8907 - val_mae: 2.1050\n",
      "Epoch 84/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0788 - mae: 1.0479 - val_loss: 7.8578 - val_mae: 2.0972\n",
      "Epoch 85/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0609 - mae: 1.0406 - val_loss: 7.8218 - val_mae: 2.0886\n",
      "Epoch 86/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0451 - mae: 1.0317 - val_loss: 7.7909 - val_mae: 2.0812\n",
      "Epoch 87/500\n",
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      "Epoch 88/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 3.0124 - mae: 1.0159 - val_loss: 7.7256 - val_mae: 2.0655\n",
      "Epoch 89/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9982 - mae: 1.0083 - val_loss: 7.6958 - val_mae: 2.0582\n",
      "Epoch 90/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9815 - mae: 1.0027 - val_loss: 7.6610 - val_mae: 2.0498\n",
      "Epoch 91/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9669 - mae: 0.9929 - val_loss: 7.6324 - val_mae: 2.0428\n",
      "Epoch 92/500\n",
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      "Epoch 93/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9384 - mae: 0.9777 - val_loss: 7.5733 - val_mae: 2.0283\n",
      "Epoch 94/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9242 - mae: 0.9708 - val_loss: 7.5432 - val_mae: 2.0208\n",
      "Epoch 95/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.9094 - mae: 0.9636 - val_loss: 7.5116 - val_mae: 2.0130\n",
      "Epoch 96/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8943 - mae: 0.9570 - val_loss: 7.4800 - val_mae: 2.0051\n",
      "Epoch 97/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8796 - mae: 0.9490 - val_loss: 7.4508 - val_mae: 1.9978\n",
      "Epoch 98/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8662 - mae: 0.9415 - val_loss: 7.4218 - val_mae: 1.9906\n",
      "Epoch 99/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8522 - mae: 0.9350 - val_loss: 7.3926 - val_mae: 1.9832\n",
      "Epoch 100/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.8395 - mae: 0.9261 - val_loss: 7.3670 - val_mae: 1.9767\n",
      "Epoch 101/500\n",
      "14/14 [==============================] - 0s 6ms/step - loss: 2.8261 - mae: 0.9200 - val_loss: 7.3368 - val_mae: 1.9691\n",
      "Epoch 102/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.8135 - mae: 0.9113 - val_loss: 7.3121 - val_mae: 1.9628\n",
      "Epoch 103/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.8013 - mae: 0.9061 - val_loss: 7.2828 - val_mae: 1.9553\n",
      "Epoch 104/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7884 - mae: 0.8982 - val_loss: 7.2571 - val_mae: 1.9487\n",
      "Epoch 105/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7762 - mae: 0.8918 - val_loss: 7.2301 - val_mae: 1.9418\n",
      "Epoch 106/500\n",
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      "Epoch 107/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7511 - mae: 0.8784 - val_loss: 7.1748 - val_mae: 1.9275\n",
      "Epoch 108/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7388 - mae: 0.8712 - val_loss: 7.1486 - val_mae: 1.9207\n",
      "Epoch 109/500\n",
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      "Epoch 110/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7154 - mae: 0.8586 - val_loss: 7.0970 - val_mae: 1.9072\n",
      "Epoch 111/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.7043 - mae: 0.8596 - val_loss: 7.0717 - val_mae: 1.9006\n",
      "Epoch 112/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6932 - mae: 0.8619 - val_loss: 7.0479 - val_mae: 1.8943\n",
      "Epoch 113/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6819 - mae: 0.8635 - val_loss: 7.0218 - val_mae: 1.8874\n",
      "Epoch 114/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6709 - mae: 0.8646 - val_loss: 6.9970 - val_mae: 1.8808\n",
      "Epoch 115/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6601 - mae: 0.8671 - val_loss: 6.9728 - val_mae: 1.8772\n",
      "Epoch 116/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6494 - mae: 0.8683 - val_loss: 6.9478 - val_mae: 1.8739\n",
      "Epoch 117/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6393 - mae: 0.8713 - val_loss: 6.9265 - val_mae: 1.8710\n",
      "Epoch 118/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6292 - mae: 0.8729 - val_loss: 6.9021 - val_mae: 1.8677\n",
      "Epoch 119/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6191 - mae: 0.8740 - val_loss: 6.8778 - val_mae: 1.8644\n",
      "Epoch 120/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.6085 - mae: 0.8767 - val_loss: 6.8542 - val_mae: 1.8613\n",
      "Epoch 121/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5989 - mae: 0.8780 - val_loss: 6.8329 - val_mae: 1.8584\n",
      "Epoch 122/500\n",
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      "Epoch 123/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5791 - mae: 0.8806 - val_loss: 6.7854 - val_mae: 1.8519\n",
      "Epoch 124/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5701 - mae: 0.8835 - val_loss: 6.7668 - val_mae: 1.8493\n",
      "Epoch 125/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5615 - mae: 0.8839 - val_loss: 6.7428 - val_mae: 1.8484\n",
      "Epoch 126/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5512 - mae: 0.8859 - val_loss: 6.7195 - val_mae: 1.8484\n",
      "Epoch 127/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5425 - mae: 0.8876 - val_loss: 6.6992 - val_mae: 1.8484\n",
      "Epoch 128/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5337 - mae: 0.8896 - val_loss: 6.6785 - val_mae: 1.8484\n",
      "Epoch 129/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5251 - mae: 0.8917 - val_loss: 6.6570 - val_mae: 1.8484\n",
      "Epoch 130/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.5167 - mae: 0.8937 - val_loss: 6.6370 - val_mae: 1.8484\n",
      "Epoch 131/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.5079 - mae: 0.8948 - val_loss: 6.6149 - val_mae: 1.8484\n",
      "Epoch 132/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4992 - mae: 0.8968 - val_loss: 6.5940 - val_mae: 1.8484\n",
      "Epoch 133/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.4910 - mae: 0.8979 - val_loss: 6.5742 - val_mae: 1.8484\n",
      "Epoch 134/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4828 - mae: 0.9010 - val_loss: 6.5548 - val_mae: 1.8484\n",
      "Epoch 135/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4751 - mae: 0.9007 - val_loss: 6.5330 - val_mae: 1.8484\n",
      "Epoch 136/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4669 - mae: 0.9037 - val_loss: 6.5152 - val_mae: 1.8484\n",
      "Epoch 137/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4597 - mae: 0.9046 - val_loss: 6.4960 - val_mae: 1.8484\n",
      "Epoch 138/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4518 - mae: 0.9059 - val_loss: 6.4757 - val_mae: 1.8484\n",
      "Epoch 139/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4443 - mae: 0.9062 - val_loss: 6.4573 - val_mae: 1.8484\n",
      "Epoch 140/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4368 - mae: 0.9097 - val_loss: 6.4386 - val_mae: 1.8484\n",
      "Epoch 141/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4299 - mae: 0.9107 - val_loss: 6.4208 - val_mae: 1.8484\n",
      "Epoch 142/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4229 - mae: 0.9126 - val_loss: 6.4032 - val_mae: 1.8484\n",
      "Epoch 143/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4160 - mae: 0.9132 - val_loss: 6.3840 - val_mae: 1.8484\n",
      "Epoch 144/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4087 - mae: 0.9146 - val_loss: 6.3664 - val_mae: 1.8484\n",
      "Epoch 145/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.4020 - mae: 0.9179 - val_loss: 6.3506 - val_mae: 1.8484\n",
      "Epoch 146/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3962 - mae: 0.9180 - val_loss: 6.3329 - val_mae: 1.8484\n",
      "Epoch 147/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3895 - mae: 0.9203 - val_loss: 6.3156 - val_mae: 1.8484\n",
      "Epoch 148/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3830 - mae: 0.9204 - val_loss: 6.2981 - val_mae: 1.8484\n",
      "Epoch 149/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3760 - mae: 0.9223 - val_loss: 6.2793 - val_mae: 1.8484\n",
      "Epoch 150/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3695 - mae: 0.9236 - val_loss: 6.2630 - val_mae: 1.8484\n",
      "Epoch 151/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3634 - mae: 0.9253 - val_loss: 6.2466 - val_mae: 1.8484\n",
      "Epoch 152/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3574 - mae: 0.9263 - val_loss: 6.2306 - val_mae: 1.8484\n",
      "Epoch 153/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3517 - mae: 0.9277 - val_loss: 6.2151 - val_mae: 1.8484\n",
      "Epoch 154/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3460 - mae: 0.9290 - val_loss: 6.1986 - val_mae: 1.8484\n",
      "Epoch 155/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3400 - mae: 0.9314 - val_loss: 6.1830 - val_mae: 1.8484\n",
      "Epoch 156/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3343 - mae: 0.9332 - val_loss: 6.1675 - val_mae: 1.8484\n",
      "Epoch 157/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3288 - mae: 0.9339 - val_loss: 6.1523 - val_mae: 1.8484\n",
      "Epoch 158/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3230 - mae: 0.9362 - val_loss: 6.1362 - val_mae: 1.8484\n",
      "Epoch 159/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.3175 - mae: 0.9369 - val_loss: 6.1202 - val_mae: 1.8484\n",
      "Epoch 160/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3121 - mae: 0.9373 - val_loss: 6.1055 - val_mae: 1.8484\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 161/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3066 - mae: 0.9398 - val_loss: 6.0910 - val_mae: 1.8484\n",
      "Epoch 162/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.3015 - mae: 0.9411 - val_loss: 6.0763 - val_mae: 1.8484\n",
      "Epoch 163/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2967 - mae: 0.9412 - val_loss: 6.0604 - val_mae: 1.8484\n",
      "Epoch 164/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2913 - mae: 0.9435 - val_loss: 6.0477 - val_mae: 1.8484\n",
      "Epoch 165/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2868 - mae: 0.9447 - val_loss: 6.0325 - val_mae: 1.8484\n",
      "Epoch 166/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2815 - mae: 0.9452 - val_loss: 6.0175 - val_mae: 1.8484\n",
      "Epoch 167/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2766 - mae: 0.9471 - val_loss: 6.0037 - val_mae: 1.8484\n",
      "Epoch 168/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2716 - mae: 0.9479 - val_loss: 5.9890 - val_mae: 1.8484\n",
      "Epoch 169/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2669 - mae: 0.9491 - val_loss: 5.9753 - val_mae: 1.8484\n",
      "Epoch 170/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2621 - mae: 0.9516 - val_loss: 5.9613 - val_mae: 1.8484\n",
      "Epoch 171/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2573 - mae: 0.9527 - val_loss: 5.9470 - val_mae: 1.8484\n",
      "Epoch 172/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2531 - mae: 0.9532 - val_loss: 5.9339 - val_mae: 1.8484\n",
      "Epoch 173/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2483 - mae: 0.9549 - val_loss: 5.9196 - val_mae: 1.8484\n",
      "Epoch 174/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2442 - mae: 0.9549 - val_loss: 5.9069 - val_mae: 1.8484\n",
      "Epoch 175/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2401 - mae: 0.9562 - val_loss: 5.8949 - val_mae: 1.8484\n",
      "Epoch 176/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2360 - mae: 0.9573 - val_loss: 5.8820 - val_mae: 1.8484\n",
      "Epoch 177/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2319 - mae: 0.9591 - val_loss: 5.8695 - val_mae: 1.8484\n",
      "Epoch 178/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2276 - mae: 0.9605 - val_loss: 5.8561 - val_mae: 1.8484\n",
      "Epoch 179/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2238 - mae: 0.9608 - val_loss: 5.8441 - val_mae: 1.8484\n",
      "Epoch 180/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2199 - mae: 0.9623 - val_loss: 5.8322 - val_mae: 1.8484\n",
      "Epoch 181/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2160 - mae: 0.9632 - val_loss: 5.8194 - val_mae: 1.8484\n",
      "Epoch 182/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2120 - mae: 0.9654 - val_loss: 5.8077 - val_mae: 1.8484\n",
      "Epoch 183/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.2084 - mae: 0.9669 - val_loss: 5.7969 - val_mae: 1.8484\n",
      "Epoch 184/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.2051 - mae: 0.9671 - val_loss: 5.7848 - val_mae: 1.8484\n",
      "Epoch 185/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.2014 - mae: 0.9679 - val_loss: 5.7744 - val_mae: 1.8484\n",
      "Epoch 186/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1984 - mae: 0.9685 - val_loss: 5.7631 - val_mae: 1.8484\n",
      "Epoch 187/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1947 - mae: 0.9709 - val_loss: 5.7526 - val_mae: 1.8484\n",
      "Epoch 188/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1913 - mae: 0.9708 - val_loss: 5.7407 - val_mae: 1.8484\n",
      "Epoch 189/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1880 - mae: 0.9725 - val_loss: 5.7303 - val_mae: 1.8484\n",
      "Epoch 190/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1848 - mae: 0.9737 - val_loss: 5.7189 - val_mae: 1.8484\n",
      "Epoch 191/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1814 - mae: 0.9737 - val_loss: 5.7072 - val_mae: 1.8484\n",
      "Epoch 192/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1780 - mae: 0.9754 - val_loss: 5.6970 - val_mae: 1.8484\n",
      "Epoch 193/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1750 - mae: 0.9760 - val_loss: 5.6868 - val_mae: 1.8484\n",
      "Epoch 194/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1721 - mae: 0.9772 - val_loss: 5.6761 - val_mae: 1.8484\n",
      "Epoch 195/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1690 - mae: 0.9777 - val_loss: 5.6659 - val_mae: 1.8484\n",
      "Epoch 196/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.1662 - mae: 0.9787 - val_loss: 5.6565 - val_mae: 1.8484\n",
      "Epoch 197/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1630 - mae: 0.9811 - val_loss: 5.6466 - val_mae: 1.8484\n",
      "Epoch 198/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1602 - mae: 0.9821 - val_loss: 5.6369 - val_mae: 1.8484\n",
      "Epoch 199/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1574 - mae: 0.9831 - val_loss: 5.6259 - val_mae: 1.8484\n",
      "Epoch 200/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1547 - mae: 0.9833 - val_loss: 5.6165 - val_mae: 1.8484\n",
      "Epoch 201/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1517 - mae: 0.9852 - val_loss: 5.6072 - val_mae: 1.8484\n",
      "Epoch 202/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1494 - mae: 0.9850 - val_loss: 5.5971 - val_mae: 1.8484\n",
      "Epoch 203/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1463 - mae: 0.9869 - val_loss: 5.5873 - val_mae: 1.8484\n",
      "Epoch 204/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1440 - mae: 0.9865 - val_loss: 5.5779 - val_mae: 1.8484\n",
      "Epoch 205/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1412 - mae: 0.9879 - val_loss: 5.5686 - val_mae: 1.8484\n",
      "Epoch 206/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1387 - mae: 0.9890 - val_loss: 5.5603 - val_mae: 1.8484\n",
      "Epoch 207/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.1367 - mae: 0.9889 - val_loss: 5.5517 - val_mae: 1.8484\n",
      "Epoch 208/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1341 - mae: 0.9905 - val_loss: 5.5431 - val_mae: 1.8484\n",
      "Epoch 209/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1315 - mae: 0.9916 - val_loss: 5.5332 - val_mae: 1.8484\n",
      "Epoch 210/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1292 - mae: 0.9921 - val_loss: 5.5244 - val_mae: 1.8484\n",
      "Epoch 211/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1268 - mae: 0.9931 - val_loss: 5.5154 - val_mae: 1.8484\n",
      "Epoch 212/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1244 - mae: 0.9944 - val_loss: 5.5070 - val_mae: 1.8484\n",
      "Epoch 213/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1219 - mae: 0.9960 - val_loss: 5.4991 - val_mae: 1.8484\n",
      "Epoch 214/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1200 - mae: 0.9965 - val_loss: 5.4910 - val_mae: 1.8484\n",
      "Epoch 215/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1180 - mae: 0.9971 - val_loss: 5.4821 - val_mae: 1.8484\n",
      "Epoch 216/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1157 - mae: 0.9976 - val_loss: 5.4734 - val_mae: 1.8484\n",
      "Epoch 217/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1139 - mae: 0.9979 - val_loss: 5.4659 - val_mae: 1.8484\n",
      "Epoch 218/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1116 - mae: 0.9992 - val_loss: 5.4579 - val_mae: 1.8484\n",
      "Epoch 219/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1095 - mae: 1.0001 - val_loss: 5.4503 - val_mae: 1.8484\n",
      "Epoch 220/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1076 - mae: 1.0007 - val_loss: 5.4419 - val_mae: 1.8484\n",
      "Epoch 221/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1056 - mae: 1.0016 - val_loss: 5.4341 - val_mae: 1.8484\n",
      "Epoch 222/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1037 - mae: 1.0025 - val_loss: 5.4262 - val_mae: 1.8484\n",
      "Epoch 223/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1016 - mae: 1.0034 - val_loss: 5.4191 - val_mae: 1.8484\n",
      "Epoch 224/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.1003 - mae: 1.0034 - val_loss: 5.4121 - val_mae: 1.8484\n",
      "Epoch 225/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0983 - mae: 1.0045 - val_loss: 5.4049 - val_mae: 1.8484\n",
      "Epoch 226/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0963 - mae: 1.0057 - val_loss: 5.3982 - val_mae: 1.8484\n",
      "Epoch 227/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0948 - mae: 1.0063 - val_loss: 5.3901 - val_mae: 1.8484\n",
      "Epoch 228/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0927 - mae: 1.0077 - val_loss: 5.3835 - val_mae: 1.8484\n",
      "Epoch 229/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0918 - mae: 1.0067 - val_loss: 5.3768 - val_mae: 1.8484\n",
      "Epoch 230/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0897 - mae: 1.0080 - val_loss: 5.3704 - val_mae: 1.8484\n",
      "Epoch 231/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0883 - mae: 1.0087 - val_loss: 5.3631 - val_mae: 1.8484\n",
      "Epoch 232/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0866 - mae: 1.0092 - val_loss: 5.3564 - val_mae: 1.8484\n",
      "Epoch 233/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0850 - mae: 1.0099 - val_loss: 5.3493 - val_mae: 1.8484\n",
      "Epoch 234/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0836 - mae: 1.0101 - val_loss: 5.3429 - val_mae: 1.8484\n",
      "Epoch 235/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0817 - mae: 1.0119 - val_loss: 5.3366 - val_mae: 1.8484\n",
      "Epoch 236/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0801 - mae: 1.0130 - val_loss: 5.3298 - val_mae: 1.8484\n",
      "Epoch 237/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0788 - mae: 1.0131 - val_loss: 5.3233 - val_mae: 1.8484\n",
      "Epoch 238/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0772 - mae: 1.0144 - val_loss: 5.3170 - val_mae: 1.8484\n",
      "Epoch 239/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0763 - mae: 1.0138 - val_loss: 5.3108 - val_mae: 1.8484\n",
      "Epoch 240/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0743 - mae: 1.0158 - val_loss: 5.3048 - val_mae: 1.8484\n",
      "Epoch 241/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0732 - mae: 1.0156 - val_loss: 5.2978 - val_mae: 1.8484\n",
      "Epoch 242/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0714 - mae: 1.0167 - val_loss: 5.2930 - val_mae: 1.8484\n",
      "Epoch 243/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0710 - mae: 1.0162 - val_loss: 5.2873 - val_mae: 1.8484\n",
      "Epoch 244/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0690 - mae: 1.0181 - val_loss: 5.2829 - val_mae: 1.8484\n",
      "Epoch 245/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0684 - mae: 1.0180 - val_loss: 5.2773 - val_mae: 1.8484\n",
      "Epoch 246/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0674 - mae: 1.0183 - val_loss: 5.2716 - val_mae: 1.8484\n",
      "Epoch 247/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0659 - mae: 1.0192 - val_loss: 5.2655 - val_mae: 1.8484\n",
      "Epoch 248/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0645 - mae: 1.0202 - val_loss: 5.2594 - val_mae: 1.8484\n",
      "Epoch 249/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0632 - mae: 1.0207 - val_loss: 5.2539 - val_mae: 1.8484\n",
      "Epoch 250/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0624 - mae: 1.0207 - val_loss: 5.2485 - val_mae: 1.8484\n",
      "Epoch 251/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0611 - mae: 1.0213 - val_loss: 5.2426 - val_mae: 1.8484\n",
      "Epoch 252/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0599 - mae: 1.0216 - val_loss: 5.2373 - val_mae: 1.8484\n",
      "Epoch 253/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0584 - mae: 1.0232 - val_loss: 5.2330 - val_mae: 1.8484\n",
      "Epoch 254/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0580 - mae: 1.0230 - val_loss: 5.2276 - val_mae: 1.8484\n",
      "Epoch 255/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0568 - mae: 1.0233 - val_loss: 5.2222 - val_mae: 1.8484\n",
      "Epoch 256/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0555 - mae: 1.0242 - val_loss: 5.2165 - val_mae: 1.8484\n",
      "Epoch 257/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0542 - mae: 1.0253 - val_loss: 5.2117 - val_mae: 1.8484\n",
      "Epoch 258/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0533 - mae: 1.0258 - val_loss: 5.2069 - val_mae: 1.8484\n",
      "Epoch 259/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0525 - mae: 1.0261 - val_loss: 5.2017 - val_mae: 1.8484\n",
      "Epoch 260/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0513 - mae: 1.0267 - val_loss: 5.1965 - val_mae: 1.8484\n",
      "Epoch 261/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0504 - mae: 1.0270 - val_loss: 5.1907 - val_mae: 1.8484\n",
      "Epoch 262/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0493 - mae: 1.0274 - val_loss: 5.1858 - val_mae: 1.8484\n",
      "Epoch 263/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0483 - mae: 1.0284 - val_loss: 5.1813 - val_mae: 1.8484\n",
      "Epoch 264/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0474 - mae: 1.0286 - val_loss: 5.1760 - val_mae: 1.8484\n",
      "Epoch 265/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0464 - mae: 1.0292 - val_loss: 5.1719 - val_mae: 1.8484\n",
      "Epoch 266/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0455 - mae: 1.0299 - val_loss: 5.1670 - val_mae: 1.8484\n",
      "Epoch 267/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0445 - mae: 1.0305 - val_loss: 5.1626 - val_mae: 1.8484\n",
      "Epoch 268/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0436 - mae: 1.0312 - val_loss: 5.1573 - val_mae: 1.8484\n",
      "Epoch 269/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0425 - mae: 1.0318 - val_loss: 5.1532 - val_mae: 1.8484\n",
      "Epoch 270/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0419 - mae: 1.0322 - val_loss: 5.1489 - val_mae: 1.8484\n",
      "Epoch 271/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0411 - mae: 1.0327 - val_loss: 5.1436 - val_mae: 1.8484\n",
      "Epoch 272/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0401 - mae: 1.0329 - val_loss: 5.1395 - val_mae: 1.8484\n",
      "Epoch 273/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0396 - mae: 1.0331 - val_loss: 5.1352 - val_mae: 1.8484\n",
      "Epoch 274/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0387 - mae: 1.0334 - val_loss: 5.1310 - val_mae: 1.8484\n",
      "Epoch 275/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0378 - mae: 1.0344 - val_loss: 5.1270 - val_mae: 1.8484\n",
      "Epoch 276/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0374 - mae: 1.0345 - val_loss: 5.1232 - val_mae: 1.8484\n",
      "Epoch 277/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0364 - mae: 1.0347 - val_loss: 5.1185 - val_mae: 1.8484\n",
      "Epoch 278/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0354 - mae: 1.0358 - val_loss: 5.1142 - val_mae: 1.8484\n",
      "Epoch 279/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0348 - mae: 1.0362 - val_loss: 5.1098 - val_mae: 1.8484\n",
      "Epoch 280/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0338 - mae: 1.0370 - val_loss: 5.1056 - val_mae: 1.8484\n",
      "Epoch 281/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0340 - mae: 1.0361 - val_loss: 5.1027 - val_mae: 1.8484\n",
      "Epoch 282/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0326 - mae: 1.0375 - val_loss: 5.0986 - val_mae: 1.8484\n",
      "Epoch 283/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0320 - mae: 1.0377 - val_loss: 5.0944 - val_mae: 1.8484\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 284/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0313 - mae: 1.0379 - val_loss: 5.0903 - val_mae: 1.8484\n",
      "Epoch 285/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0303 - mae: 1.0391 - val_loss: 5.0869 - val_mae: 1.8484\n",
      "Epoch 286/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0302 - mae: 1.0388 - val_loss: 5.0831 - val_mae: 1.8484\n",
      "Epoch 287/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0293 - mae: 1.0396 - val_loss: 5.0788 - val_mae: 1.8484\n",
      "Epoch 288/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0288 - mae: 1.0393 - val_loss: 5.0748 - val_mae: 1.8484\n",
      "Epoch 289/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0275 - mae: 1.0412 - val_loss: 5.0722 - val_mae: 1.8484\n",
      "Epoch 290/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0277 - mae: 1.0404 - val_loss: 5.0689 - val_mae: 1.8484\n",
      "Epoch 291/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0269 - mae: 1.0410 - val_loss: 5.0647 - val_mae: 1.8484\n",
      "Epoch 292/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0261 - mae: 1.0416 - val_loss: 5.0611 - val_mae: 1.8484\n",
      "Epoch 293/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0257 - mae: 1.0418 - val_loss: 5.0573 - val_mae: 1.8484\n",
      "Epoch 294/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0249 - mae: 1.0421 - val_loss: 5.0533 - val_mae: 1.8484\n",
      "Epoch 295/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0243 - mae: 1.0426 - val_loss: 5.0496 - val_mae: 1.8484\n",
      "Epoch 296/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0238 - mae: 1.0430 - val_loss: 5.0458 - val_mae: 1.8484\n",
      "Epoch 297/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0231 - mae: 1.0435 - val_loss: 5.0423 - val_mae: 1.8484\n",
      "Epoch 298/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0222 - mae: 1.0447 - val_loss: 5.0392 - val_mae: 1.8484\n",
      "Epoch 299/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0222 - mae: 1.0443 - val_loss: 5.0360 - val_mae: 1.8484\n",
      "Epoch 300/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0215 - mae: 1.0449 - val_loss: 5.0325 - val_mae: 1.8484\n",
      "Epoch 301/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0210 - mae: 1.0450 - val_loss: 5.0292 - val_mae: 1.8484\n",
      "Epoch 302/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0203 - mae: 1.0458 - val_loss: 5.0262 - val_mae: 1.8484\n",
      "Epoch 303/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0198 - mae: 1.0462 - val_loss: 5.0233 - val_mae: 1.8484\n",
      "Epoch 304/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0193 - mae: 1.0467 - val_loss: 5.0201 - val_mae: 1.8484\n",
      "Epoch 305/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0194 - mae: 1.0462 - val_loss: 5.0178 - val_mae: 1.8484\n",
      "Epoch 306/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0186 - mae: 1.0470 - val_loss: 5.0144 - val_mae: 1.8484\n",
      "Epoch 307/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0186 - mae: 1.0466 - val_loss: 5.0123 - val_mae: 1.8484\n",
      "Epoch 308/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0177 - mae: 1.0475 - val_loss: 5.0088 - val_mae: 1.8484\n",
      "Epoch 309/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0174 - mae: 1.0475 - val_loss: 5.0058 - val_mae: 1.8484\n",
      "Epoch 310/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0167 - mae: 1.0483 - val_loss: 5.0024 - val_mae: 1.8484\n",
      "Epoch 311/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0166 - mae: 1.0484 - val_loss: 5.0004 - val_mae: 1.8484\n",
      "Epoch 312/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0161 - mae: 1.0486 - val_loss: 4.9977 - val_mae: 1.8484\n",
      "Epoch 313/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0161 - mae: 1.0486 - val_loss: 4.9955 - val_mae: 1.8484\n",
      "Epoch 314/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0150 - mae: 1.0496 - val_loss: 4.9925 - val_mae: 1.8484\n",
      "Epoch 315/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0149 - mae: 1.0496 - val_loss: 4.9893 - val_mae: 1.8484\n",
      "Epoch 316/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0142 - mae: 1.0503 - val_loss: 4.9866 - val_mae: 1.8484\n",
      "Epoch 317/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0142 - mae: 1.0501 - val_loss: 4.9845 - val_mae: 1.8484\n",
      "Epoch 318/500\n",
      "14/14 [==============================] - 0s 5ms/step - loss: 2.0136 - mae: 1.0509 - val_loss: 4.9816 - val_mae: 1.8484\n",
      "Epoch 319/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0130 - mae: 1.0513 - val_loss: 4.9791 - val_mae: 1.8484\n",
      "Epoch 320/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0131 - mae: 1.0511 - val_loss: 4.9766 - val_mae: 1.8484\n",
      "Epoch 321/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0126 - mae: 1.0513 - val_loss: 4.9740 - val_mae: 1.8484\n",
      "Epoch 322/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0119 - mae: 1.0524 - val_loss: 4.9712 - val_mae: 1.8484\n",
      "Epoch 323/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0119 - mae: 1.0521 - val_loss: 4.9686 - val_mae: 1.8484\n",
      "Epoch 324/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0113 - mae: 1.0525 - val_loss: 4.9658 - val_mae: 1.8484\n",
      "Epoch 325/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0110 - mae: 1.0527 - val_loss: 4.9631 - val_mae: 1.8484\n",
      "Epoch 326/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0104 - mae: 1.0534 - val_loss: 4.9602 - val_mae: 1.8484\n",
      "Epoch 327/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0105 - mae: 1.0531 - val_loss: 4.9580 - val_mae: 1.8484\n",
      "Epoch 328/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0096 - mae: 1.0543 - val_loss: 4.9557 - val_mae: 1.8484\n",
      "Epoch 329/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0095 - mae: 1.0544 - val_loss: 4.9529 - val_mae: 1.8484\n",
      "Epoch 330/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0093 - mae: 1.0540 - val_loss: 4.9508 - val_mae: 1.8484\n",
      "Epoch 331/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0091 - mae: 1.0542 - val_loss: 4.9487 - val_mae: 1.8484\n",
      "Epoch 332/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0085 - mae: 1.0551 - val_loss: 4.9462 - val_mae: 1.8484\n",
      "Epoch 333/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0086 - mae: 1.0547 - val_loss: 4.9441 - val_mae: 1.8484\n",
      "Epoch 334/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0078 - mae: 1.0558 - val_loss: 4.9418 - val_mae: 1.8484\n",
      "Epoch 335/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0073 - mae: 1.0563 - val_loss: 4.9401 - val_mae: 1.8484\n",
      "Epoch 336/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0074 - mae: 1.0562 - val_loss: 4.9381 - val_mae: 1.8484\n",
      "Epoch 337/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0072 - mae: 1.0560 - val_loss: 4.9359 - val_mae: 1.8484\n",
      "Epoch 338/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0070 - mae: 1.0564 - val_loss: 4.9337 - val_mae: 1.8484\n",
      "Epoch 339/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0065 - mae: 1.0569 - val_loss: 4.9312 - val_mae: 1.8484\n",
      "Epoch 340/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0065 - mae: 1.0565 - val_loss: 4.9295 - val_mae: 1.8484\n",
      "Epoch 341/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0059 - mae: 1.0574 - val_loss: 4.9275 - val_mae: 1.8484\n",
      "Epoch 342/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0059 - mae: 1.0572 - val_loss: 4.9250 - val_mae: 1.8484\n",
      "Epoch 343/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0054 - mae: 1.0576 - val_loss: 4.9229 - val_mae: 1.8484\n",
      "Epoch 344/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0052 - mae: 1.0579 - val_loss: 4.9203 - val_mae: 1.8484\n",
      "Epoch 345/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0045 - mae: 1.0589 - val_loss: 4.9182 - val_mae: 1.8484\n",
      "Epoch 346/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0046 - mae: 1.0583 - val_loss: 4.9166 - val_mae: 1.8484\n",
      "Epoch 347/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0043 - mae: 1.0593 - val_loss: 4.9141 - val_mae: 1.8484\n",
      "Epoch 348/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0041 - mae: 1.0587 - val_loss: 4.9119 - val_mae: 1.8484\n",
      "Epoch 349/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0036 - mae: 1.0593 - val_loss: 4.9103 - val_mae: 1.8484\n",
      "Epoch 350/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0033 - mae: 1.0602 - val_loss: 4.9081 - val_mae: 1.8484\n",
      "Epoch 351/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0034 - mae: 1.0597 - val_loss: 4.9069 - val_mae: 1.8484\n",
      "Epoch 352/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0031 - mae: 1.0599 - val_loss: 4.9053 - val_mae: 1.8484\n",
      "Epoch 353/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0032 - mae: 1.0597 - val_loss: 4.9032 - val_mae: 1.8484\n",
      "Epoch 354/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0028 - mae: 1.0599 - val_loss: 4.9011 - val_mae: 1.8484\n",
      "Epoch 355/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0023 - mae: 1.0607 - val_loss: 4.8994 - val_mae: 1.8484\n",
      "Epoch 356/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 2.0018 - mae: 1.0617 - val_loss: 4.8978 - val_mae: 1.8484\n",
      "Epoch 357/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 2.0020 - mae: 1.0612 - val_loss: 4.8955 - val_mae: 1.8484\n",
      "Epoch 358/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0017 - mae: 1.0613 - val_loss: 4.8935 - val_mae: 1.8484\n",
      "Epoch 359/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0018 - mae: 1.0610 - val_loss: 4.8919 - val_mae: 1.8484\n",
      "Epoch 360/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0015 - mae: 1.0614 - val_loss: 4.8901 - val_mae: 1.8484\n",
      "Epoch 361/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0015 - mae: 1.0611 - val_loss: 4.8888 - val_mae: 1.8484\n",
      "Epoch 362/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0010 - mae: 1.0618 - val_loss: 4.8871 - val_mae: 1.8484\n",
      "Epoch 363/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0011 - mae: 1.0617 - val_loss: 4.8856 - val_mae: 1.8484\n",
      "Epoch 364/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0006 - mae: 1.0623 - val_loss: 4.8839 - val_mae: 1.8484\n",
      "Epoch 365/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0005 - mae: 1.0624 - val_loss: 4.8824 - val_mae: 1.8484\n",
      "Epoch 366/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0001 - mae: 1.0627 - val_loss: 4.8811 - val_mae: 1.8484\n",
      "Epoch 367/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0004 - mae: 1.0625 - val_loss: 4.8798 - val_mae: 1.8484\n",
      "Epoch 368/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 2.0000 - mae: 1.0627 - val_loss: 4.8780 - val_mae: 1.8484\n",
      "Epoch 369/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9998 - mae: 1.0631 - val_loss: 4.8763 - val_mae: 1.8484\n",
      "Epoch 370/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9995 - mae: 1.0634 - val_loss: 4.8750 - val_mae: 1.8484\n",
      "Epoch 371/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9993 - mae: 1.0639 - val_loss: 4.8735 - val_mae: 1.8484\n",
      "Epoch 372/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9995 - mae: 1.0635 - val_loss: 4.8723 - val_mae: 1.8484\n",
      "Epoch 373/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9994 - mae: 1.0634 - val_loss: 4.8715 - val_mae: 1.8484\n",
      "Epoch 374/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9991 - mae: 1.0638 - val_loss: 4.8698 - val_mae: 1.8484\n",
      "Epoch 375/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9986 - mae: 1.0644 - val_loss: 4.8682 - val_mae: 1.8484\n",
      "Epoch 376/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9985 - mae: 1.0645 - val_loss: 4.8665 - val_mae: 1.8484\n",
      "Epoch 377/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9985 - mae: 1.0648 - val_loss: 4.8647 - val_mae: 1.8484\n",
      "Epoch 378/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9986 - mae: 1.0642 - val_loss: 4.8640 - val_mae: 1.8484\n",
      "Epoch 379/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9980 - mae: 1.0652 - val_loss: 4.8629 - val_mae: 1.8484\n",
      "Epoch 380/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9981 - mae: 1.0649 - val_loss: 4.8623 - val_mae: 1.8484\n",
      "Epoch 381/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9980 - mae: 1.0651 - val_loss: 4.8607 - val_mae: 1.8484\n",
      "Epoch 382/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9980 - mae: 1.0651 - val_loss: 4.8593 - val_mae: 1.8484\n",
      "Epoch 383/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9978 - mae: 1.0652 - val_loss: 4.8580 - val_mae: 1.8484\n",
      "Epoch 384/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9978 - mae: 1.0652 - val_loss: 4.8568 - val_mae: 1.8484\n",
      "Epoch 385/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9972 - mae: 1.0658 - val_loss: 4.8556 - val_mae: 1.8484\n",
      "Epoch 386/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9983 - mae: 1.0650 - val_loss: 4.8561 - val_mae: 1.8484\n",
      "Epoch 387/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9974 - mae: 1.0656 - val_loss: 4.8542 - val_mae: 1.8484\n",
      "Epoch 388/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9969 - mae: 1.0662 - val_loss: 4.8527 - val_mae: 1.8484\n",
      "Epoch 389/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9969 - mae: 1.0664 - val_loss: 4.8511 - val_mae: 1.8484\n",
      "Epoch 390/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9971 - mae: 1.0662 - val_loss: 4.8506 - val_mae: 1.8484\n",
      "Epoch 391/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9964 - mae: 1.0667 - val_loss: 4.8500 - val_mae: 1.8484\n",
      "Epoch 392/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9967 - mae: 1.0666 - val_loss: 4.8487 - val_mae: 1.8484\n",
      "Epoch 393/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9965 - mae: 1.0669 - val_loss: 4.8472 - val_mae: 1.8484\n",
      "Epoch 394/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9964 - mae: 1.0670 - val_loss: 4.8460 - val_mae: 1.8484\n",
      "Epoch 395/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9962 - mae: 1.0673 - val_loss: 4.8445 - val_mae: 1.8484\n",
      "Epoch 396/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9958 - mae: 1.0678 - val_loss: 4.8433 - val_mae: 1.8484\n",
      "Epoch 397/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9959 - mae: 1.0677 - val_loss: 4.8420 - val_mae: 1.8484\n",
      "Epoch 398/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9966 - mae: 1.0668 - val_loss: 4.8422 - val_mae: 1.8484\n",
      "Epoch 399/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9957 - mae: 1.0676 - val_loss: 4.8405 - val_mae: 1.8484\n",
      "Epoch 400/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9957 - mae: 1.0680 - val_loss: 4.8389 - val_mae: 1.8484\n",
      "Epoch 401/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9961 - mae: 1.0674 - val_loss: 4.8390 - val_mae: 1.8484\n",
      "Epoch 402/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9952 - mae: 1.0683 - val_loss: 4.8376 - val_mae: 1.8484\n",
      "Epoch 403/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9952 - mae: 1.0684 - val_loss: 4.8360 - val_mae: 1.8484\n",
      "Epoch 404/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9953 - mae: 1.0683 - val_loss: 4.8350 - val_mae: 1.8484\n",
      "Epoch 405/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9952 - mae: 1.0680 - val_loss: 4.8344 - val_mae: 1.8484\n",
      "Epoch 406/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9949 - mae: 1.0685 - val_loss: 4.8329 - val_mae: 1.8484\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 407/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9949 - mae: 1.0686 - val_loss: 4.8319 - val_mae: 1.8484\n",
      "Epoch 408/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9948 - mae: 1.0689 - val_loss: 4.8306 - val_mae: 1.8484\n",
      "Epoch 409/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9949 - mae: 1.0687 - val_loss: 4.8296 - val_mae: 1.8484\n",
      "Epoch 410/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9944 - mae: 1.0692 - val_loss: 4.8283 - val_mae: 1.8484\n",
      "Epoch 411/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9944 - mae: 1.0695 - val_loss: 4.8270 - val_mae: 1.8484\n",
      "Epoch 412/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9942 - mae: 1.0696 - val_loss: 4.8256 - val_mae: 1.8484\n",
      "Epoch 413/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9942 - mae: 1.0698 - val_loss: 4.8244 - val_mae: 1.8484\n",
      "Epoch 414/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9942 - mae: 1.0695 - val_loss: 4.8236 - val_mae: 1.8484\n",
      "Epoch 415/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9936 - mae: 1.0704 - val_loss: 4.8225 - val_mae: 1.8484\n",
      "Epoch 416/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9938 - mae: 1.0704 - val_loss: 4.8210 - val_mae: 1.8484\n",
      "Epoch 417/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9938 - mae: 1.0701 - val_loss: 4.8199 - val_mae: 1.8484\n",
      "Epoch 418/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9934 - mae: 1.0707 - val_loss: 4.8185 - val_mae: 1.8484\n",
      "Epoch 419/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9935 - mae: 1.0704 - val_loss: 4.8179 - val_mae: 1.8484\n",
      "Epoch 420/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9940 - mae: 1.0698 - val_loss: 4.8183 - val_mae: 1.8484\n",
      "Epoch 421/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9936 - mae: 1.0700 - val_loss: 4.8175 - val_mae: 1.8484\n",
      "Epoch 422/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9939 - mae: 1.0701 - val_loss: 4.8173 - val_mae: 1.8484\n",
      "Epoch 423/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9935 - mae: 1.0701 - val_loss: 4.8164 - val_mae: 1.8484\n",
      "Epoch 424/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9931 - mae: 1.0709 - val_loss: 4.8152 - val_mae: 1.8484\n",
      "Epoch 425/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9935 - mae: 1.0705 - val_loss: 4.8150 - val_mae: 1.8484\n",
      "Epoch 426/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9930 - mae: 1.0708 - val_loss: 4.8140 - val_mae: 1.8484\n",
      "Epoch 427/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9933 - mae: 1.0705 - val_loss: 4.8132 - val_mae: 1.8484\n",
      "Epoch 428/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9928 - mae: 1.0716 - val_loss: 4.8119 - val_mae: 1.8484\n",
      "Epoch 429/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9930 - mae: 1.0710 - val_loss: 4.8109 - val_mae: 1.8484\n",
      "Epoch 430/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9925 - mae: 1.0717 - val_loss: 4.8103 - val_mae: 1.8484\n",
      "Epoch 431/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9927 - mae: 1.0715 - val_loss: 4.8092 - val_mae: 1.8484\n",
      "Epoch 432/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9928 - mae: 1.0713 - val_loss: 4.8084 - val_mae: 1.8484\n",
      "Epoch 433/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9924 - mae: 1.0719 - val_loss: 4.8070 - val_mae: 1.8484\n",
      "Epoch 434/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9923 - mae: 1.0720 - val_loss: 4.8059 - val_mae: 1.8484\n",
      "Epoch 435/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9922 - mae: 1.0722 - val_loss: 4.8048 - val_mae: 1.8484\n",
      "Epoch 436/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9926 - mae: 1.0719 - val_loss: 4.8047 - val_mae: 1.8484\n",
      "Epoch 437/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9922 - mae: 1.0721 - val_loss: 4.8038 - val_mae: 1.8484\n",
      "Epoch 438/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9920 - mae: 1.0723 - val_loss: 4.8026 - val_mae: 1.8484\n",
      "Epoch 439/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9918 - mae: 1.0726 - val_loss: 4.8019 - val_mae: 1.8484\n",
      "Epoch 440/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9925 - mae: 1.0720 - val_loss: 4.8022 - val_mae: 1.8484\n",
      "Epoch 441/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9918 - mae: 1.0724 - val_loss: 4.8012 - val_mae: 1.8484\n",
      "Epoch 442/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9918 - mae: 1.0727 - val_loss: 4.8001 - val_mae: 1.8484\n",
      "Epoch 443/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9918 - mae: 1.0727 - val_loss: 4.7990 - val_mae: 1.8484\n",
      "Epoch 444/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9916 - mae: 1.0729 - val_loss: 4.7982 - val_mae: 1.8484\n",
      "Epoch 445/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9913 - mae: 1.0730 - val_loss: 4.7979 - val_mae: 1.8484\n",
      "Epoch 446/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9919 - mae: 1.0726 - val_loss: 4.7978 - val_mae: 1.8484\n",
      "Epoch 447/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9917 - mae: 1.0726 - val_loss: 4.7971 - val_mae: 1.8484\n",
      "Epoch 448/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9915 - mae: 1.0730 - val_loss: 4.7961 - val_mae: 1.8484\n",
      "Epoch 449/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9915 - mae: 1.0731 - val_loss: 4.7953 - val_mae: 1.8484\n",
      "Epoch 450/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9912 - mae: 1.0735 - val_loss: 4.7945 - val_mae: 1.8484\n",
      "Epoch 451/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9912 - mae: 1.0732 - val_loss: 4.7943 - val_mae: 1.8484\n",
      "Epoch 452/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9912 - mae: 1.0735 - val_loss: 4.7937 - val_mae: 1.8484\n",
      "Epoch 453/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9911 - mae: 1.0737 - val_loss: 4.7925 - val_mae: 1.8484\n",
      "Epoch 454/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9910 - mae: 1.0739 - val_loss: 4.7913 - val_mae: 1.8484\n",
      "Epoch 455/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9910 - mae: 1.0742 - val_loss: 4.7902 - val_mae: 1.8484\n",
      "Epoch 456/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9910 - mae: 1.0738 - val_loss: 4.7896 - val_mae: 1.8484\n",
      "Epoch 457/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9905 - mae: 1.0743 - val_loss: 4.7895 - val_mae: 1.8484\n",
      "Epoch 458/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9916 - mae: 1.0733 - val_loss: 4.7905 - val_mae: 1.8484\n",
      "Epoch 459/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9910 - mae: 1.0737 - val_loss: 4.7900 - val_mae: 1.8484\n",
      "Epoch 460/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9907 - mae: 1.0742 - val_loss: 4.7890 - val_mae: 1.8484\n",
      "Epoch 461/500\n",
      "14/14 [==============================] - 0s 2ms/step - loss: 1.9908 - mae: 1.0741 - val_loss: 4.7881 - val_mae: 1.8484\n",
      "Epoch 462/500\n",
      "14/14 [==============================] - 0s 3ms/step - loss: 1.9907 - mae: 1.0741 - val_loss: 4.7874 - val_mae: 1.8484\n",
      "Epoch 463/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9906 - mae: 1.0744 - val_loss: 4.7866 - val_mae: 1.8484\n",
      "Epoch 464/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9906 - mae: 1.0744 - val_loss: 4.7860 - val_mae: 1.8484\n",
      "Epoch 465/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9905 - mae: 1.0746 - val_loss: 4.7851 - val_mae: 1.8484\n",
      "Epoch 466/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9901 - mae: 1.0751 - val_loss: 4.7847 - val_mae: 1.8484\n",
      "Epoch 467/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9904 - mae: 1.0747 - val_loss: 4.7842 - val_mae: 1.8484\n",
      "Epoch 468/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9907 - mae: 1.0743 - val_loss: 4.7840 - val_mae: 1.8484\n",
      "Epoch 469/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9905 - mae: 1.0747 - val_loss: 4.7833 - val_mae: 1.8484\n",
      "Epoch 470/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9900 - mae: 1.0750 - val_loss: 4.7829 - val_mae: 1.8484\n",
      "Epoch 471/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9906 - mae: 1.0745 - val_loss: 4.7829 - val_mae: 1.8484\n",
      "Epoch 472/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9903 - mae: 1.0745 - val_loss: 4.7824 - val_mae: 1.8484\n",
      "Epoch 473/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9904 - mae: 1.0748 - val_loss: 4.7815 - val_mae: 1.8484\n",
      "Epoch 474/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9906 - mae: 1.0741 - val_loss: 4.7816 - val_mae: 1.8484\n",
      "Epoch 475/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9905 - mae: 1.0744 - val_loss: 4.7814 - val_mae: 1.8484\n",
      "Epoch 476/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9901 - mae: 1.0749 - val_loss: 4.7812 - val_mae: 1.8484\n",
      "Epoch 477/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9902 - mae: 1.0749 - val_loss: 4.7802 - val_mae: 1.8484\n",
      "Epoch 478/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9903 - mae: 1.0747 - val_loss: 4.7794 - val_mae: 1.8484\n",
      "Epoch 479/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9898 - mae: 1.0752 - val_loss: 4.7788 - val_mae: 1.8484\n",
      "Epoch 480/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9900 - mae: 1.0754 - val_loss: 4.7779 - val_mae: 1.8484\n",
      "Epoch 481/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9905 - mae: 1.0749 - val_loss: 4.7784 - val_mae: 1.8484\n",
      "Epoch 482/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9898 - mae: 1.0752 - val_loss: 4.7772 - val_mae: 1.8484\n",
      "Epoch 483/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9899 - mae: 1.0751 - val_loss: 4.7769 - val_mae: 1.8484\n",
      "Epoch 484/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9896 - mae: 1.0758 - val_loss: 4.7760 - val_mae: 1.8484\n",
      "Epoch 485/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9899 - mae: 1.0754 - val_loss: 4.7753 - val_mae: 1.8484\n",
      "Epoch 486/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9893 - mae: 1.0761 - val_loss: 4.7745 - val_mae: 1.8484\n",
      "Epoch 487/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9896 - mae: 1.0759 - val_loss: 4.7744 - val_mae: 1.8484\n",
      "Epoch 488/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9898 - mae: 1.0756 - val_loss: 4.7738 - val_mae: 1.8484\n",
      "Epoch 489/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9897 - mae: 1.0758 - val_loss: 4.7732 - val_mae: 1.8484\n",
      "Epoch 490/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9897 - mae: 1.0757 - val_loss: 4.7728 - val_mae: 1.8484\n",
      "Epoch 491/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9894 - mae: 1.0761 - val_loss: 4.7715 - val_mae: 1.8484\n",
      "Epoch 492/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9898 - mae: 1.0754 - val_loss: 4.7712 - val_mae: 1.8484\n",
      "Epoch 493/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9893 - mae: 1.0759 - val_loss: 4.7708 - val_mae: 1.8484\n",
      "Epoch 494/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9891 - mae: 1.0762 - val_loss: 4.7705 - val_mae: 1.8484\n",
      "Epoch 495/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9896 - mae: 1.0760 - val_loss: 4.7705 - val_mae: 1.8484\n",
      "Epoch 496/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9893 - mae: 1.0764 - val_loss: 4.7696 - val_mae: 1.8484\n",
      "Epoch 497/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9889 - mae: 1.0767 - val_loss: 4.7694 - val_mae: 1.8484\n",
      "Epoch 498/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9892 - mae: 1.0767 - val_loss: 4.7687 - val_mae: 1.8484\n",
      "Epoch 499/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9894 - mae: 1.0760 - val_loss: 4.7684 - val_mae: 1.8484\n",
      "Epoch 500/500\n",
      "14/14 [==============================] - 0s 4ms/step - loss: 1.9888 - mae: 1.0770 - val_loss: 4.7681 - val_mae: 1.8484\n"
     ]
    }
   ],
   "source": [
    "from keras import backend as K\n",
    "K.clear_session()\n",
    "\n",
    "num_epochs = 500\n",
    "all_mae_histories = []\n",
    "for i in range(k):\n",
    "    print('processing fold #', i)\n",
    "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
    "\n",
    "    partial_train_data = np.concatenate(\n",
    "        [train_data[:i * num_val_samples],\n",
    "         train_data[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "    partial_train_targets = np.concatenate(\n",
    "        [train_targets[:i * num_val_samples],\n",
    "         train_targets[(i + 1) * num_val_samples:]],\n",
    "        axis=0)\n",
    "\n",
    "    model = build_model()\n",
    "\n",
    "    history = model.fit(partial_train_data, partial_train_targets,\n",
    "                        validation_data=(val_data, val_targets),\n",
    "                        epochs=num_epochs, batch_size=1, verbose=1)\n",
    "    mae_history = history.history['val_mae']\n",
    "    all_mae_histories.append(mae_history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_mae_history = [np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "average_mae_history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation MAE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def smooth_curve(points, factor=0.9):\n",
    "  smoothed_points = []\n",
    "  for point in points:\n",
    "    if smoothed_points:\n",
    "      previous = smoothed_points[-1]\n",
    "      smoothed_points.append(previous * factor + point * (1 - factor))\n",
    "    else:\n",
    "      smoothed_points.append(point)\n",
    "  return smoothed_points\n",
    "\n",
    "smooth_mae_history = smooth_curve(average_mae_history[0:100])\n",
    "\n",
    "plt.plot(range(1, len(smooth_mae_history) + 1), smooth_mae_history)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Validation MAE')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 8.3301 - mae: 2.4174\n",
      "Epoch 2/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 8.2206 - mae: 2.3950\n",
      "Epoch 3/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 8.1403 - mae: 2.3786\n",
      "Epoch 4/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 8.0643 - mae: 2.3632\n",
      "Epoch 5/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 8.0184 - mae: 2.3540\n",
      "Epoch 6/600\n",
      "2/2 [==============================] - 0s 4ms/step - loss: 7.9651 - mae: 2.3422\n",
      "Epoch 7/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.9191 - mae: 2.3324\n",
      "Epoch 8/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 7.8792 - mae: 2.3243\n",
      "Epoch 9/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 7.8414 - mae: 2.3159\n",
      "Epoch 10/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 7.8101 - mae: 2.3089\n",
      "Epoch 11/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.7799 - mae: 2.3024\n",
      "Epoch 12/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.7382 - mae: 2.2937\n",
      "Epoch 13/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 7.6845 - mae: 2.2834\n",
      "Epoch 14/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.6449 - mae: 2.2752\n",
      "Epoch 15/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.6132 - mae: 2.2683\n",
      "Epoch 16/600\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 7.5869 - mae: 2.2621\n",
      "Epoch 17/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.5666 - mae: 2.2577\n",
      "Epoch 18/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.5455 - mae: 2.2530\n",
      "Epoch 19/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 7.5216 - mae: 2.2481\n",
      "Epoch 20/600\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "2/2 [==============================] - 0s 1ms/step - loss: 1.4750 - mae: 0.8660\n",
      "Epoch 537/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4746 - mae: 0.8666\n",
      "Epoch 538/600\n",
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      "Epoch 539/600\n",
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      "Epoch 540/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4687 - mae: 0.8643\n",
      "Epoch 541/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4691 - mae: 0.8613\n",
      "Epoch 542/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4621 - mae: 0.8649\n",
      "Epoch 543/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4593 - mae: 0.8660\n",
      "Epoch 544/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4531 - mae: 0.8690\n",
      "Epoch 545/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4472 - mae: 0.8720\n",
      "Epoch 546/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4424 - mae: 0.8743\n",
      "Epoch 547/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4381 - mae: 0.8766\n",
      "Epoch 548/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4375 - mae: 0.8750\n",
      "Epoch 549/600\n",
      "2/2 [==============================] - 0s 994us/step - loss: 1.4380 - mae: 0.8740\n",
      "Epoch 550/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4375 - mae: 0.8717\n",
      "Epoch 551/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4324 - mae: 0.8739\n",
      "Epoch 552/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4294 - mae: 0.8753\n",
      "Epoch 553/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4244 - mae: 0.8784\n",
      "Epoch 554/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4234 - mae: 0.8787\n",
      "Epoch 555/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4226 - mae: 0.8786\n",
      "Epoch 556/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4223 - mae: 0.8779\n",
      "Epoch 557/600\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.4218 - mae: 0.8767\n",
      "Epoch 558/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4191 - mae: 0.8778\n",
      "Epoch 559/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4176 - mae: 0.8777\n",
      "Epoch 560/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4181 - mae: 0.8764\n",
      "Epoch 561/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4132 - mae: 0.8810\n",
      "Epoch 562/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4121 - mae: 0.8766\n",
      "Epoch 563/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4114 - mae: 0.8769\n",
      "Epoch 564/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4115 - mae: 0.8754\n",
      "Epoch 565/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4065 - mae: 0.8785\n",
      "Epoch 566/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4068 - mae: 0.8760\n",
      "Epoch 567/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4055 - mae: 0.8757\n",
      "Epoch 568/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4048 - mae: 0.8757\n",
      "Epoch 569/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4046 - mae: 0.8746\n",
      "Epoch 570/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.4037 - mae: 0.8747\n",
      "Epoch 571/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.4027 - mae: 0.8744\n",
      "Epoch 572/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.4007 - mae: 0.8750\n",
      "Epoch 573/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3997 - mae: 0.8715\n",
      "Epoch 574/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3986 - mae: 0.8715\n",
      "Epoch 575/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3971 - mae: 0.8714\n",
      "Epoch 576/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3965 - mae: 0.8720\n",
      "Epoch 577/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3950 - mae: 0.8707\n",
      "Epoch 578/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3910 - mae: 0.8726\n",
      "Epoch 579/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.3920 - mae: 0.8733\n",
      "Epoch 580/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3827 - mae: 0.8775\n",
      "Epoch 581/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3825 - mae: 0.8766\n",
      "Epoch 582/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.3821 - mae: 0.8749\n",
      "Epoch 583/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3787 - mae: 0.8776\n",
      "Epoch 584/600\n",
      "2/2 [==============================] - 0s 1000us/step - loss: 1.3771 - mae: 0.8734\n",
      "Epoch 585/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3777 - mae: 0.8723\n",
      "Epoch 586/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3736 - mae: 0.8723\n",
      "Epoch 587/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3725 - mae: 0.8732\n",
      "Epoch 588/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3719 - mae: 0.8728\n",
      "Epoch 589/600\n",
      "2/2 [==============================] - 0s 999us/step - loss: 1.3712 - mae: 0.8724\n",
      "Epoch 590/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3695 - mae: 0.8721\n",
      "Epoch 591/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3657 - mae: 0.8756\n",
      "Epoch 592/600\n",
      "2/2 [==============================] - 0s 994us/step - loss: 1.3625 - mae: 0.8788\n",
      "Epoch 593/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3618 - mae: 0.8785\n",
      "Epoch 594/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3622 - mae: 0.8773\n",
      "Epoch 595/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3627 - mae: 0.8760\n",
      "Epoch 596/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3617 - mae: 0.8737\n",
      "Epoch 597/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3620 - mae: 0.8718\n",
      "Epoch 598/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3593 - mae: 0.8719\n",
      "Epoch 599/600\n",
      "2/2 [==============================] - 0s 2ms/step - loss: 1.3594 - mae: 0.8707\n",
      "Epoch 600/600\n",
      "2/2 [==============================] - 0s 1ms/step - loss: 1.3601 - mae: 0.8695\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x1e7fd780f08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = build_model()\n",
    "# Train it on the entirety of the data.\n",
    "model.fit(train_data, train_targets,\n",
    "          epochs=600, batch_size=16, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict=model.predict(test_data)\n",
    "predict"
   ]
  }
 ],
 "metadata": {
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   "display_name": "Python 3",
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   "name": "python3"
  },
  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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